Hygiene management for reducing illnesses and infections caused by ineffective hygiene practices

ABSTRACT

A wearable computing device may be used to track the efficacy of one or more cleaning actions performed. The device can include one or more sensors that detect and measure motion associated with a cleaning event. In different examples, the movement data generated by the device can be compared to reference movement data to determine if the individual has cleaned all the objects they were expected to clean and/or if the individual has cleaned a given object sufficiently well. As another example, the movement data generated by the device can be analyzed to distinguish cleaning and non-cleaning movement actions as well as to distinguish different types of cleaning actions during cleaning movement. The quality of each cleaning action can be evaluated. In any configuration, the device may perform an operation in response to determining that ineffective cleaning is being performed, causing corrected cleaning action to be performed.

CROSS-REFERENCE

This application claims the benefit of U.S. Provisional PatentApplication No. 62/801,865, filed Feb. 6, 2019, and U.S. ProvisionalPatent Application No. 62/801,875, filed Feb. 6, 2019, the entirecontents of each of which are incorporated herein by reference.

TECHNICAL FIELD

This disclosure relates to devices and techniques for managing hygieneactivity, including monitoring and controlling of cleaning efficacythrough a wearable computing device worn by an individual performingcleaning.

BACKGROUND

Ineffective cleaning is one of the leading causes of pathogentransmission, resulting in illnesses and infections for millionsannually. For example, the United States Centers for Disease Control andPrevention estimates that 48 million people annually get sick in theUnited States due to foodborne illness, leading to 128,000hospitalizations and 3000 deaths. Further, the World Health Organizationestimates that hundreds of millions of patients are affected byhealth-care associated infections worldwide each year, with 7-10% of allhospitalized patients acquiring at least one health-care associatedinfection during their hospitalization. Viruses and bacteria can alsoreadily pass through other public or semi-public spaces, such asairports, sports stadiums, museums, and hotels if care is not taken tomanage pathogen transmission pathways.

Implementing robust and aggressive hygiene practices are the best way toprotect against the acquisition and transmission of pathogens. The typesof hygiene practices used will depend on the environment of operationbut may include systematic handwashing, controlled food preparationtechniques, systematic cleaning and sterilization of contact surfaces inthe environment, and the like. While plans and practices can beestablished for managing hygiene activity in an environment, the lack ofhygiene compliance surveillance systems makes tracking and controllingcompliance challenging. The challenges associated with ensuring hygienecompliance are exacerbated by the fact that the employees assignedcleaning tasks are often entry-level positions, characterized by highturnover and, in some cases, limited motivation and dedication toperforming the assigned tasks.

SUMMARY

In general, this disclosure is directed to devices, systems, andtechniques for managing hygiene activity by deploying a computing deviceassociated with an individual performing cleaning to track the efficacyof their cleaning actions. The computing device can include one or moresensors that detect and measure cleaning motion associated movement ofthe computing device caused by movement of the individual, e.g., duringa cleaning event. In some examples, the computing device is worn by theindividual performing the cleaning, such as at a location between theirshoulder and tip of their fingers (e.g., wrist, upper arm). In eithercase, the computing device can detect movement associated with theindividual going about their assigned tasks, which may include movementduring cleaning activities as well as interstitial movements betweencleaning activities. The movement data generated by the computing devicecan be analyzed to determine an efficacy of the cleaning being performedby the individual. In some configurations, an operation of the computingdevice is controlled based on the efficacy of the cleaning determined,causing the individual performing the cleaning to modify their cleaningactivity in response to the operation. Additionally or alternatively,the efficacy of the cleaning determined can be stored for the cleaningevent, providing cleaning validation information for the environmentbeing cleaned.

The types of hygiene activities monitored during a cleaning event mayvary depending on the hygiene practices established for the environmentbeing cleaned. As one example, the individual performing cleaning may beassigned a certain number of target surfaces to be cleaned. For example,in the case of a healthcare environment, the surfaces to be cleaned mayinclude a light switch, a table top, a bed rail, a door knob, amedication dispensing pole, a faucet handle, and the like. In the caseof a food preparation environment (e.g., restaurant, catering facility),the surface may include food preparation counters, floor surfaces, afryer, a grill, stove surfaces, microwave surfaces, refrigeratorsurfaces, and the like. In either case, the individual performingcleaning may be assigned a number of surfaces to be cleaned.

During operation, the computing device can generate a signalcorresponding to movement of the device caused by the individualperforming cleaning carrying out their tasks. Each surface targeted forcleaning may have a different movement signal associated with cleaningof that target surface. Movement data generated by the computing devicecan be compared with reference movement data associated with each targetsurface. If the movement data indicates that the individual performingcleaning has missed a target surface, the computing device may performan operation. For example, the computing device may provide an alert insubstantially real time instructing the user to complete cleaning of themissed target surface.

Additionally or alternatively, the quality of cleaning of any particulartarget surface may also be determined using movement data generated bythe computing device during the cleaning operation. For example, themovement data generated by the computing device during cleaning of aparticular surface can be compared with reference movement dataassociated with a quality of cleaning of that target surface. Thereference movement data associated with the quality of cleaning maycorrespond to a thoroughness with which the target surface is cleanedand/or an extent or area of the target surface.

In some applications, the individual carrying the computing device maybe tasked with performing cleaning and non-cleaning tasks and/orperforming multiple different cleaning tasks. For example, a protocolfor the individual may dictate that they clean one or more targetsurfaces in the environment then perform an individual hand sanitizingevent on themselves before turning to other tasks. The computing devicecan generate a signal corresponding to movement during this entirecourse of activity. Movement data generated by the computing device canbe compared with reference movement data to classify and distinguishbetween cleaning and non-cleaning actions. The movement data identifiedas corresponding to a cleaning action can further by analyzed todetermine the specific type of cleaning action performed (e.g., surfacecleaning as opposed to hand cleaning). In some examples, the quality ofthat specific cleaning action is further evaluated with reference tomovement data associated with a quality of cleaning for that specificcleaning action. In this way, a total hygiene management system may beprovided to monitor and/or control multiple different types of hygieneactivity.

The addition of hygiene compliance surveillance and control, asdescribed herein, can allow users of the technology to reduce incidentsof pathogen transmission through ineffective or incomplete cleaning. Forexample, organizations that run food preparation environments can seereduced incidents of foodborne illness associated with their facilityafter deploying the technology as compared to before deploying thetechnology. As another example, healthcare organizations can see reducedincidents of health care-associated infections after deploying thetechnology as compared to before deploying the technology. Otherenvironments and applications can also benefit from the technology.

In one example, a method of reducing illnesses and infections caused byineffective cleaning through tracked cleaning efficacy is described. Themethod includes detecting, by a wearable computing device that is wornby an individual performing cleaning on a plurality of target surfaces,movement associated with the wearable device during a cleaning event.The method also involves determining, based on the movement associatedwith the wearable computing device, whether the individual has performeda cleaning operation on each of the plurality of target surfaces by atleast comparing movement data generated by the wearable device withreference movement data associated with cleaning of each of theplurality of target surfaces. In addition, the method involves,responsive to determining that the individual has not performed thecleaning operation on at least one of the plurality of target surfaces,performing, by the wearable computing device, an operation.

In another example, a wearable computing device is described. The deviceincludes at least one sensor configured to detect movement associatedwith the wearable computing device, at least one processor, and a memorycomprising instructions that, when executed, cause at least oneprocessor to perform certain actions. The example specifies that theactions include receiving, from the at least one sensor, movement datafor the wearable computing device while an individual wearing thewearable computing device performs a cleaning operation on a pluralityof target surfaces during a cleaning event. The actions also includedetermining, based on the movement data, whether the individual hasperformed the cleaning operation on each of the plurality of targetsurfaces by at least comparing movement data with reference movementdata associated with cleaning of each of the plurality of targetsurfaces. The actions also involve, responsive to determining that theindividual has not performed the cleaning operation on at least one ofthe plurality of target surfaces, performing an operation.

In another example, a method of establishing a customer-specific systemfor tracking cleaning efficacy is described. The method includesperforming, by an individual wearing a wearable computing device, acleaning operation on each of a plurality of target surfaces, theplurality of target surfaces being selected as target surfaces of whichcleaning is desired to be tracked in connection with subsequent cleaningevents. The method also includes generating, by the wearable computingdevice, movement data associated with movement of the wearable deviceduring the cleaning operation performed on each of a plurality of targetsurfaces. The method further involves associating different portions ofthe movement data generated during the cleaning operation with aparticular one of each of the plurality of target surfaces on which theindividual has performed the cleaning operation. In addition, the methodinvolves determining, for each of the plurality of different targetsurfaces, reference data indicative of the cleaning operation beingperformed from the associated different portion of movement data foreach of the plurality of different target surfaces. The method furtherincludes storing the reference data for each of the plurality ofdifferent target surfaces for use in connection with subsequent cleaningevents.

In another example, a method of controlling cleaning effectiveness isdescribed. The method includes detecting, by a wearable computing devicethat is worn by an individual performing cleaning on a target surface,movement associated with the wearable device during a cleaning event.The method also includes determining, based on the movement associatedwith the wearable computing device, a quality of cleaning for the targetsurface by at least comparing movement data generated by the wearabledevice with reference movement data associated with a threshold qualityof cleaning for the target surface. The method further involves,responsive to determining that the target surface has not beeneffectively cleaned to the threshold quality of cleaning, performing, bythe wearable computing device, an operation.

In another example, a wearable computing device is described. The deviceincludes at least one sensor configured to detect movement associatedwith the wearable computing device, at least one processor, and a memorycomprising instructions that, when executed, cause the at least oneprocessor to perform certain actions. The actions include receiving,from the at least one sensor, movement data for the wearable computingdevice while an individual wearing the wearable computing deviceperforms a cleaning operation on a target surface during a cleaningevent. The actions also include determining, based on the movement data,a quality of cleaning for the target surface by at least comparingmovement data with reference movement data associated with a thresholdquality of cleaning for the target surface. The actions further include,responsive to determining that the target surface has not beeneffectively cleaned to the threshold quality of cleaning, performing anoperation.

In another example, a method of total hygiene management is described.The method involves determining, based on movement of a wearablecomputing device, at least one feature of movement that indicates awearer of the wearable computing device is performing a cleaning action,thereby distinguishing movement of the wearable computing device duringnon-cleaning actions. The method includes determining, based oncomparison of the feature(s) of movement with reference to movement dataassociated with different types of cleaning actions, a specific type ofcleaning action performed by the wearer of the wearable computingdevice. The method also includes determining a quality of cleaning forthe specific type of cleaning action performed by at least comparingmovement data generated by the wearable device during the specific typeof cleaning action with reference movement data associated with athreshold quality of cleaning for the specific type of cleaning action.The method further includes, responsive to determining that the specifictype of cleaning action performed by the wearer of the wearablecomputing device does not satisfy the threshold quality of cleaning,performing, by the wearable computing device, an operation.

In another example, a wearable computing device is described. The deviceincludes at least one sensor configured to detect movement associatedwith the wearable computing device, at least one processor, and a memorycomprising instructions that, when executed, cause the at least oneprocessor to perform certain actions. The actions include receiving,from the at least one sensor, movement data associated with the wearablecomputing device, and determining, based on the movement data, at leastone feature of movement that indicates the individual wearing thewearable computing device is performing a cleaning action, therebydistinguishing movement of the wearable computing device duringnon-cleaning actions. The actions further include determining, based oncomparison of the feature of movement with reference to movement dataassociated with different types of cleaning actions, a specific type ofcleaning action performed by the wearer of the wearable computingdevice. The actions also involve determining, a quality of cleaning forthe specific type of cleaning action performed by at least comparing themovement data generated during the specific type of cleaning action toreference movement data associated with a threshold quality of cleaningfor the specific type of cleaning action. The actions further include,responsive to determining that the specific type of cleaning actionperformed does not satisfy the threshold quality of cleaning, performingan operation.

The details of one or more examples are set forth in the accompanyingdrawings and the description below. Other features, objects, andadvantages will be apparent from the description and drawings, and fromthe claims.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a conceptual diagram illustrating an example computing systemthat is configured to track cleaning efficacy of an individualperforming cleaning during a cleaning event

FIG. 2 is a block diagram illustrating an example wearable computingdevice configured according to one or more aspects of the presentdisclosure.

FIGS. 3A-3C illustrate example surfaces and/or equipment that may becleaned, optionally using example tools, the cleaning efficacy of whichis evaluated according to the present disclosure.

FIG. 4 is a flow diagram illustrating an example process for training anexample wearable computing device to subsequently determine whether anindividual performing cleaning has cleaned each of a plurality of targetsurfaces intended to be clean as part of an established protocol

FIG. 5 is a flow diagram illustrating an example process for training anexample wearable computing device to subsequently determine whether anindividual performing cleaning has effectively cleaned the targetsurface to a threshold quality of cleaning.

FIG. 6 is a flow diagram illustrating an example process for training anexample wearable computing device to subsequently evaluate a pluralityof different cleaning actions, e.g., as part of a total hygienemanagement system.

FIG. 7 illustrates an example hand hygiene protocol that may bespecified for a wearer of a wearable computing device.

FIG. 8 is a flowchart illustrating an example operation of an examplewearable computing device configured to track cleaning efficacy forreducing illnesses and infections caused by ineffective cleaning inaccordance with one or more aspects of the present disclosure.

FIG. 9 is a flowchart illustrating another example operation of anexample wearable computing device configured to track cleaning efficacyfor reducing illnesses and infections caused by ineffective cleaning inaccordance with one or more additional aspects of the presentdisclosure.

FIG. 10 is a flowchart illustrating example operation of an examplewearable computing device configured to track cleaning efficacy fortotal hygiene management in accordance with one or more aspects of thepresent disclosure.

FIGS. 11 and 12 are plots of the linear acceleration and rotation ratedata, respectively, generated during an experiment.

FIG. 13 illustrates an example single time-domain feature representationgenerated from raw sample data for the experiment of FIGS. 11 and 12.

FIG. 14 illustrates the top two features determined from the candidatefeatures for binary classification for the experimental data of FIGS.11-13.

FIG. 15 is a plot showing discrimination of three example types of toolsused as part of a mock restaurant context floorcare study utilizingmovement data.

FIG. 16 is a plot showing discrimination of five example target surfacesperformed as part of a mock hospital context study utilizing movementdata.

FIGS. 17A-17D illustrate of an example sequential series of userinterface graphics that may be displayed to a user to help guideexecution of a cleaning protocol.

DETAILED DESCRIPTION

In general, this disclosure is directed to devices, systems, andtechniques utilizing a wearable computing device (e.g., an activitytracker, a computerized watch, etc.) to detect movement associate withan individual while performing one or more hygiene-related tasks. Acomputing system (e.g., a server, a mobile phone, etc.) may communicatewith a wearable computing device (e.g., activity tracker, watch) via anetwork. The wearable computing device may, over time, detect movements(e.g., accelerations, angular velocity, changes in tilt, etc.) and mayprovide information about the detected movements (e.g., as movementdata) to the computing system via the network. In some implementations,the computing system and/or wearable computing device may identifyfeatures of the movement data corresponding to specific hygiene activitybeing performed.

For example, the computing system may determine whether certain objectstargeted for cleaning have, in fact, been cleaned, e.g., by comparingmovement data associated with cleaning of each target object withreference movement data corresponding to cleaning of that object. Asanother example, the computing system may determine whether a particularobject targeted for cleaning has been effectively cleaned, e.g., bycomparing movement data associated with a level of cleaning of thattarget object with reference movement data corresponding to a thresholdlevel of cleaning for the object.

As yet a further example, the computing system may distinguish differenttypes of hygiene activities performed during a course of movement andevaluate hygiene compliance associated with one or more of those hygieneactivities. For instance, the computing system may determine that awearer of the computing device has performed a first type of cleaningaction (e.g., floor surface cleaning, cleaning of the equipment) and asecond type of cleaning action (e.g., cleaning of the wearer's hands).The computing system may determine a quality of one or both cleaningactions and/or an order in which the cleaning actions were performed.The computing system may further determine whether the quality of thecleaning action(s) and/or order of cleaning conforms tohygiene-compliance standards set for the environment in which theactions were performed.

In some implementations, the computing system generates and storescleaning validation information associated with the environment in whichone or more hygiene actions were performed. Unlike some cleaningcompliance programs presently used that do not have an ability tosurveil or validate that targeted cleaning actions were, in fact,performed, techniques according to the present disclosure may providedata-validated evidence of cleaning compliance. The cleaning compliancedata may be stored information corresponding to one or more cleaningactions performed indicating, e.g., that certain surfaces and/or objectswere cleaned during a cleaning event, a quality of cleaning of one ormore surfaces and/or objects, and/or a type of cleaning actionperformed. The cleaning compliance data may also include a timestampcorresponding to when the cleaning action was performed and/or datacorresponding to the actual cleaning movement performed during thecleaning action and/or other metadata corresponding to the context ofmeasurement (e.g., room identification, GPS location). In this way, acleaning provider can provide validation information evidencing thehygiene services performed and an owner or operator of a location canhave evidence of hygiene compliance for their establishment.

In addition to or in lieu of providing cleaning validation information,a computing system according to the disclosure may invoke, or thewearable computing device may initiate, performance of an operationbased on cleaning efficacy information determined based on movement datadetected by the wearable cleaning device during a cleaning event. Forexample, the wearable cleaning device may activate a user alert featureand/or output information to an individual wearing the device indicatingbreach of a cleaning compliance standard. Such breach may indicate thatthe individual performing cleaning has missed a surface targeted forcleaning, not cleaned a target surface to a threshold level of cleaningquality, and/or performed a wrong sequence of cleaning actions (e.g.,performed a hand hygiene cleaning action before an equipment cleaningaction rather than vice versa). In some implementations, the wearablecleaning device may perform the operation to notify the wearer of thebreach substantially in real time with the breach occurring. As aresult, the wearer may take immediate corrective action to address thecleaning compliance breach. Additionally or alternatively, the operationperformed by the wearable cleaning device may issue training to thewearer of the wearable cleaning device providing instructing to the useron corrective actions to be performed.

By providing cleaning compliance surveillance and control according toone or more aspects of the present disclosure, users of the technologymay reduce incidents of pathogen transmission through ineffective orincomplete cleaning. For example, organizations that run foodpreparation environments can see reduced incidents of foodborne illnessassociated with their facility after deploying the technology ascompared to before deploying the technology. As another example,healthcare organizations can see reduced incidents of healthcare-associated infections after deploying the technology as compared tobefore deploying the technology. Infection and/or illness ratesattributed to ineffective cleaning may be reduced by at least 20% afterdeploying the technology as compared to prior to deploying thetechnology, such as at least 40%, at least 60%, at least 80%, or atleast 90%.

Throughout the disclosure, examples are described where a computingsystem (e.g., a server, etc.) and/or computing device (e.g., a wearablecomputing device, etc.) may analyze information (e.g., accelerations,orientations, etc.) associated with the computing system and/orcomputing device. Such examples may be implemented so that the computingsystem and/or computing device can only perform the analyses afterreceiving permission from a user (e.g., a person wearing the wearablecomputing device) to analyze the information. For example, in situationsdiscussed below in which the mobile computing device may collect or maymake use of information associated with the user and the computingsystem and/or computing device, the user may be provided with anopportunity to provide input to control whether programs or features ofthe computing system and/or computing device can collect and make use ofuser information (e.g., information about a user's occupation, contacts,work hours, work history, training history, the user's preferences,and/or the user's past and current location), or to dictate whetherand/or how to the computing system and/or computing device may receivecontent that may be relevant to the user. In addition, certain data maybe treated in one or more ways before it is stored or used by thecomputing system and/or computing device, so thatpersonally-identifiable information is removed. For example, a user'sidentity may be treated so that no personally identifiable informationcan be determined about the user, or a user's geographic location may begeneralized where location information is obtained (such as to a city,ZIP code, or state level), so that a particular location of a usercannot be determined. Thus, the user may have control over howinformation is collected about the user and used by the computing systemand/or computing device.

FIG. 1 is a conceptual diagram illustrating an example computing system10, which is configured to track cleaning efficacy of an individualperforming cleaning during a cleaning event. System 10 includes awearable computing device 12, which can be worn by the individualperforming cleaning and can generate data indicative of thatindividual's movement during the cleaning event, in accordance with oneor more aspects of the present disclosure. System 10 also includesremote computing system 14 and network 16.

FIG. 1 shows wearable computing device 12 as being located within anenvironment 18 in which one or more hygiene actions (e.g., surfacecleaning) may be performed. In the illustrated example, environment 18is depicted as a healthcare environment having a bedroom 20 and abathroom 22. Bedroom 20 may have one or more target surface intended tobe cleaned during a cleaning event, such as a television remote control20A, a bed rail 20B, and a medication support pole 20C, to name a fewexemplary surfaces. Similarly, bathroom 22 may have one or more targetsurfaces intended to be cleaned during a cleaning event, such as asink/faucet 22A and a toilet 22B, to again name a couple examplesurfaces. Such a healthcare environment may be susceptible tocontraction of healthcare-acquired infections, making rigorouscompliance with hygiene and cleaning protocols important for patientwell-being. That being said, the techniques of the present disclosureare not limited to such an exemplary environment. Rather, the techniquesof the disclosure may be utilized at any location where it is desirableto have validated evidence of hygiene compliance. Example environmentsin which aspects of the present disclosure may be utilized include, butare not limited to, a food preparation environment, a hotel-roomenvironment, a food processing plant, and a dairy farm.

Wearable computing device 12 may be any type of computing device, whichcan be worn, held, or otherwise physically attached to a person, andwhich includes one or more processors configured to process and analyzeindications of movement (e.g., sensor data) of the wearable computingdevice. Examples of wearable computing device 12 include, but are notlimited to, a watch, an activity tracker, computerized eyewear, acomputerized glove, computerized jewelry (e.g., a computerized ring), amobile phone, or any other combination of hardware, software, and/orfirmware that can be used to detect movement of a person who is wearing,holding, or otherwise being attached to wearable computing device 12.Such wearable computing device may be attached to a person's finger,wrist, arm, torso, or other bodily location sufficient to detect motionassociated with the wearer's actions during the performance of acleaning event. In some examples, wearable computing device 12 may havea housing attached to a band that is physically secured to (e.g., about)a portion of the wearer's body. In other examples, wearable computingdevice 12 may be insertable into a pocket of an article of clothing wornby the wearer without having a separate securing band physicallyattaching the wearable computing device to the wearer.

Although shown in FIG. 1 as a separate element apart from remotecomputing system 14, in some examples, some or all of the functionalityof remote computing system 14 may be implemented by wearable computingdevice 12. For example, module 26 and data stores 28, 30, and 32 mayexist locally at wearable computing device 12, to receive informationregarding movement of the wearable computing device and to performanalyses as described herein. Accordingly, while certain functionalitiesare described herein as being performed by wearable computing device 12and remote computing system 14, respectively, some or all of thefunctionalities may be shifted from the remote computing system to thewearable computing device, or vice versa, without departing from thescope of disclosure.

The phrase “cleaning action” as used herein refers to an act of cleaninghaving motion associated with it in multiple dimensions and which may ormay not utilize a tool to perform the cleaning. Some examples ofcleaning actions include an individual cleaning a specific object (e.g.,door knob, toilet), optionally with a specific tool (e.g., rag, brush,mop), and an individual cleaning a portion of their body (e.g., washinghands). A cleaning action can include preparatory motion that occursbefore delivery of a cleaning force, such as spraying a cleaner on asurface, wringing water from a mop, filling a bucket, soaking a rag,etc.

The term “substantially real time” as used herein means while anindividual is still performing cleaning or is in sufficiently closetemporal proximity to the termination of the cleaning that theindividual is still in or proximate to the environment in which thecleaning occurred to perform a corrective cleaning operation.

The phrase “health care environment” as used herein in connection with asurface to be cleaned refers to a surface of an instrument, a device, acart, a cage, furniture, a structure, a building, or the like that isemployed as part of a health care activity. Examples of health caresurfaces include surfaces of medical or dental instruments, of medicalor dental devices, of electronic apparatus employed for monitoringpatient health, and of floors, walls, or fixtures of structures in whichhealth care occurs. Health care surfaces are found in hospital,surgical, infirmity, birthing, mortuary, and clinical diagnosis rooms aswell as nursing and elderly care facilities. These surfaces can be thosetypified as “hard surfaces” (such as walls, floors, bed-pans, etc.), orfabric surfaces, e.g., knit, woven, and non-woven surfaces (such assurgical garments, draperies, bed linens, bandages, etc.), orpatient-care equipment (such as respirators, diagnostic equipment,shunts, body scopes, wheel chairs, beds, etc.), or surgical anddiagnostic equipment. Health care surfaces include articles and surfacesemployed in animal health care.

The phrase “food preparation environment” as used herein in connectionwith a surface to be cleaned refers to a surface of a tool, a machine,equipment, a structure, a building, or the like that is employed as partof a food processing, preparation, or storage activity. Examples of foodprocessing surfaces include surfaces of food processing or preparationequipment (e.g., slicing, canning, or transport equipment, includingflumes), of food processing wares (e.g., utensils, dishware, wash ware,and bar glasses), and of floors, walls, or fixtures of structures inwhich food processing occurs. Example food processing surfaces are foundin ovens, fryers, grills, microwaves, refrigerators, countertops,storage receptacles, sinks, beverage chillers and warmers, meat chillingor scalding waters.

The phrase “cleaning operation” as used herein means the performance ofa motion indicative of and corresponding to a cleaning motion. Acleaning motion can be one which an individual performs to aid in soilremoval, pathogen population reduction, and combinations thereof.

The phrase “reference movement data” as used herein refers to both rawsensor data corresponding to the reference movement(s) and data derivedfrom or based on the raw sensor data corresponding to the referencemovement(s). In implementations where reference movement data is derivedfrom or based on the raw sensor data, the reference movement data mayprovide a more compact representation of the raw sensor data. Forexample, reference movement data may be stored in the form of one ormore window-granularity features, coefficients in a model, or othermathematical transformations of the raw reference data.

In FIG. 1, network 16 represents any public or private communicationnetwork. Wearable computing device 12 and remote computing system 14 maysend and receive data across network 16 using any suitable communicationtechniques. For example, wearable computing device 12 may be operativelycoupled to network 16 using network link 24A. Remote computing system 14may be operatively coupled to network 16 by network link 24B. Network 16may include network hubs, network switches, network routers, etc., thatare operatively inter-coupled thereby providing for the exchange ofinformation between wearable computing device 12 and remote computingsystem 14. In some examples, network links 24A and 24B may be Ethernet,Bluetooth, ATM or other network connections. Such connections may bewireless and/or wired connections.

Remote computing system 14 of system 10 represents any suitable mobileor stationary remote computing system, such as one or more desktopcomputers, laptop computers, mobile computers (e.g., mobile phone),mainframes, servers, cloud computing systems, etc. capable of sendingand receiving information across network link 24B to network 16. In someexamples, remote computing system 14 represents a cloud computing systemthat provides one or more services through network 16. One or morecomputing devices, such as wearable computing device 12, may access theone or more services provided by the cloud using remote computing system14. For example, wearable computing device 12 may store and/or accessdata in the cloud using remote computing system 14. In some examples,some or all the functionality of remote computing system 14 exists in amobile computing platform, such as a mobile phone, tablet computer, etc.that may or may not be at the same geographical location as wearablecomputing device 12. For instance, some or all the functionality ofremote computing system 14 may, in some examples, reside in and beexecute from within a mobile computing device that is in environment 18with wearable computing device 12 and/or reside in and be implemented inthe wearable device itself.

In some implementations, wearable computing device 12 can generate andstore data indicative of movement for processing by remote computingsystem 14 even when the wearable computing device is not incommunication with the remote computing system. In practice, forexample, wearable computing device 12 may periodically lose connectivitywith remote computing system 14 and/or network 16. In these and othersituations, wearable computing device 12 may operate in anoffline/disconnected state to perform the same functions or more limitedfunctions the wearable computing device performs if online/connectedwith remote computing system 14. When connection is reestablishedbetween computing device 12 and remote computing system 14, thecomputing device can forward the stored data generated during the periodwhen the device was offline. In different examples, computing device 12may reestablish connection with remote computing system 14 when wirelessconnectivity is reestablished via network 16 or when the computingdevice is connected to a docketing station to facilitate downloading ofinformation temporarily stored on the computing device.

Remote computing system 14 in the example of FIG. 1 includes cleaningefficacy determination module 26 and one or more data stores, which isillustrated as including a target surfaces comparison data store 28, acleaning quality comparison data store 30, and a cleaning actioncomparison data store 32. Cleaning efficacy determination module 26 mayperform operations described using software, hardware, firmware, or amixture of hardware, software, and firmware residing in and/or executingat remote computing system 14. Remote computing system 14 may executecleaning efficacy determination module 26 with multiple processors ormultiple devices. Remote computing system 14 may execute cleaningefficacy determination module 26 as a virtual machine executing onunderlying hardware. Cleaning efficacy determination module 26 mayexecute as a service of an operating system or computing platform.Cleaning efficacy determination module 26 may execute as one or moreexecutable programs at an application layer of a computing platform.

Features described as data stores can represent any suitable storagemedium for storing actual, modeled, or otherwise derived data thatcleaning efficacy determination module 26 may access to determinewhether a wearer of wearable computing device 12 has performed compliantcleaning behavior. For example, the data stores may contain lookuptables, databases, charts, graphs, functions, equations, and the likethat cleaning efficacy determination module 26 may access to evaluatedata generated by wearable computing device 12. Cleaning efficacydetermination module 26 may rely on features generated from theinformation contained in one or more data stores to determine whethersensor data obtained from wearable computing device 12 indicates that aperson has performed certain cleaning compliance behaviors, such ascleaning all surfaces targeted for cleaning, cleaning one or more targetsurfaces appropriately thoroughly, and/or performing certain specificcleaning actions. The data stored in the data stores may be generatedfrom and/or based on one or more training sessions, as described ingreater detail with respect to FIGS. 4-6. Remote computing system 14 mayprovide access to the data stored at the data stores as a cloud-basedservice to devices connected to network 16, such as wearable computingdevice 12.

Cleaning efficacy determination module 26 may respond to requests forinformation (e.g., from wearable computing device 12) indicating whetheran individual performing cleaning and wearing or having worn wearablecomputing device 12 has performed compliant cleaning activity. Cleaningefficacy determination module 26 may receive sensor data via link 24Band network 16 from wearable computing device 12 and compare the sensordata to one or more comparison data sets stored in data stores of theremote computing system 14. Cleaning efficacy determination module 26may respond to the request by sending information from remote computingsystem 14 to wearable computing device 12 through network 16 via links.

Cleaning efficacy determination module 26 may be implemented todetermine a number of different characteristics of cleaning behavior andcompliance with cleaning protocols based on information detected bywearable computing device 12. In general, wearable computing device 12may output, for transmission to remote computing system 14, informationindicative of movement of the wearer (e.g., data indicative of adirection, location, orientation, position, elevation, etc. of wearablecomputing device 12), as discussed in greater detail below. Cleaningefficacy determination module 26 may discriminate movement associatedwith cleaning action from movement not associated with cleaning actionduring the cleaning event, or period over which movement data iscaptured, e.g., with reference to stored data in remote computing system14. Cleaning efficacy determination module 26 may further analyze themovement data associated with cleaning action to determine whether suchaction is in compliance with one or more standards, e.g., based oncomparative data stored in one or more data stores.

In one implementation, an individual performing cleaning may be assigneda schedule of multiple surfaces to be cleaned during a cleaning event.The schedule of surfaces to be cleaned may correspond to surfaces thatare frequently touched by individuals in the environment and that aresubject to contamination, or otherwise desired to be cleaned as part ofa cleaning compliance protocol. The individual performing cleaning maybe instructed on which surfaces should be cleaned during a cleaningevent and, optionally, and order in which the surfaces should be cleanedand/or a thoroughness with which each surface should be cleaned.

During performance of the cleaning event, wearable computing device 12may output information corresponding to movement of the wearablecomputing device. Cleaning efficacy determination module 26 may receivemovement data from wearable computing device 12 and analyze the movementdata with reference to target surface comparative data stored at datastore 28. Target surface comparative data store 28 may contain datacorresponding to cleaning for each of the target surfaces scheduled bythe individual performing cleaning to be cleaned.

In some examples, cleaning efficacy determination module 26 determinesone or more features of the movement data corresponding to cleaning of aparticular surface. Each surface targeted for cleaning may havedimensions and/or an orientation within three-dimensional space uniqueto that target surface and which distinguishes it from each other targetsurface intended to be cleaned. Accordingly, movement associated withcleaning of each target surface may provide a unique signature, orcomparative data set, that distinguishes movement associated withcleaning of each target surface within the data set. The specificfeatures of the data defining the target surface may vary, e.g.,depending on the characteristics of the target surface andcharacteristics of sensor data generated by wearable computing device12. Target surface comparative data store 28 may contain datacorresponding to cleaning of each target surface intended to be cleaned.For example, target surface comparative data store 28 may containfeatures generated from reference movement data associated with cleaningof each of the multiple target surfaces scheduled to be cleaned.

Cleaning efficacy determination module 26 can analyze one or morefeatures of movement data generated during a cleaning event relative tothe features in target surface comparative data store 28 to determinewhich of the target surfaces the individual has performed a cleaning on.Cleaning efficacy determination module 26 can determine if one or moretarget surfaces scheduled to be cleaned were cleaned or were not, infact, cleaned based on reference to target surface comparison data store28. Remote computing system 14 may communicate with wearable computingdevice 12 to initiate an operation via the wearable computing device inthe event that at least one target surface scheduled to be cleaned wasdetermined to not have been cleaned during the cleaning event.

As another implementation, an individual performing cleaning may beinstructed on a quality with which a target surface should be cleanedduring a cleaning event. The quality of cleaning may be instructedthrough a cleaning protocol training the individual on how to properlyclean the target surface. Example characteristics of the cleaningprotocol may specify a technique to be used to clean the target surface,an amount of force to be applied via a cleaning implement when cleaningthe target surface, an extent or area of the target surface to becleaned, and/or a duration of cleaning that should be performed on thetarget surface.

In some examples, a cleaning protocol may specify a sequence of one ormore activities to be performed and/or a particular cleaning techniqueor series of techniques to be used when performing the one or morecleaning activities. Example cleaning activities that may be specifiedas part of a cleaning protocol include an order of surfaces to becleaned (e.g., cleaning room from top-to-bottom, wet-to-dry, and/orleast-to-most soiled). Example cleaning techniques that may be specifiedinclude a specific type of cleaning to be used on a particular surface(e.g., a scrubbing action, using overlapping strokes) and/or asequential series of cleaning steps to be performed on the particularsurface (e.g., removing visible soils followed by disinfection).

During performance of a cleaning event, wearable computing device 12 canoutput information corresponding to movement of the wearable computingdevice. Cleaning efficacy determination module 26 may receive movementdata from wearable computing device 12 and analyze the movement datawith reference to cleaning quality comparative data stored at data store30. Cleaning quality comparative data store 30 may contain datacorresponding to a quality of cleaning for the target surface intendedto be cleaned by the individual performing clean.

In some examples, cleaning efficacy determination module 26 determinesone or more features of the movement data corresponding to quality ofcleaning of a surface. The movement data may be indicative of amount ofwork, or intensity, of the cleaning action performed. Additionally oralternatively, the movement data may be indicative of an area of thesurface being cleaned (e.g., dimensions and orientation inthree-dimensional space), which may indicate whether the individualperforming cleaning has cleaned an entirety of the target surface. Stillfurther additionally or alternatively, the movement data may beindicative of the type of cleaning technique, or series of differentcleaning techniques, performed on the surface. The specific features ofthe data defining the quality of cleaning may vary, e.g., depending onthe characteristics of the cleaning protocol dictating the qualitycleaning, the characteristics of the surface being cleaned, and/or thecharacteristics of the sensor data generated by wearable computingdevice 12.

Cleaning quality comparison data store 30 may contain data correspondingto the quality of cleaning of each surface, the quality of cleaning ofwhich is intended to be evaluated. Cleaning quality comparison datastore 30 may contain features generated from reference movement dataassociated with a compliant quality of cleaning for each surface, thequality of cleaning of which is intended to be evaluated. The referencemovement data may correspond to a threshold level of cleaning indicatedby the originator of the reference movement data as corresponding to asuitable or compliant level of quality.

Cleaning efficacy determination module 26 can analyze one or morefeatures of movement data generated during a cleaning event relative tofeatures in cleaning quality comparison data store 30 to determinewhether the surface on which the individual performed cleaning has beencleaned to a threshold level of quality. Cleaning efficacy determinationmodule 26 can determine if a target surface was cleaned to a thresholdlevel of quality or if the surface was not cleaned to the thresholdlevel of quality based on reference to cleaning quality comparison datastore 30. Remote computing system 14 may communicate with wearablecomputing device 12 to initiate an operation via the wearable computingdevice in the event that a target surface was determined to not havebeen cleaned to the threshold level of quality.

As another example implementation, an individual performing cleaning maybe assigned multiple cleaning actions to be performed as part of aprotocol of work. Each specific type of cleaning action may be differentthan each other specific type of cleaning action and, in some examples,may desirably be performed in a specified order. For example, one typeof cleaning action that may be performed is an environmental cleaningaction in which one or more surfaces in environment 18 are desired to becleaned. Examples of these types of cleaning actions include floorsurface cleaning actions (e.g., sweeping, mopping) and non-floor surfacecleaning actions (e.g., cleaning equipment within an environment 18).Another type of cleaning action that may be performed is a personalcleaning action, such as a hand hygiene cleaning event in which anindividual conducts a handwashing protocol (e.g., with analcohol-containing sanitizer, with soap and water). As part of a totalhygiene management program, the efficacy and/or order of each of thedifferent types of cleaning actions performed the individual may beevaluated.

For example, wearable computing device 12 may output informationcorresponding to movement of the wearable computing device during aperiod of time in which the wearer performs multiple cleaning actions aswell as non-cleaning actions. Cleaning efficacy determination module 26may receive movement data from wearable computing device 12 and analyzethe movement data with reference to cleaning action comparison datastore 32. Cleaning action comparison data store 32 may contain datacorresponding to multiple different types of cleaning actions that maybe performed by an individual wearing wearable computing device 12. Eachtype of cleaning action may have a movement signature associated with itthat is stored in cleaning action comparison data store 32.

Cleaning efficacy determination module 26 may distinguish movement dataassociated with cleaning actions from movement data associated withnon-cleaning actions with reference to cleaning action comparison datastore 32. Cleaning efficacy determination module 26 may furtherdetermine a specific type of cleaning action(s) performed by the wearerof wearable computing device 12 with reference to cleaning actioncomparison data store 32. In some implementations, cleaning efficacydetermination module 26 may further determine a quality of clean for oneor more of the specific types of cleaning actions performed by the warewith further reference to cleaning quality comparison data store 30.

In some examples, cleaning efficacy determination module 26 determinesone or more features of the movement data corresponding to the multiplecleaning actions performed by the wearer. Each cleaning action may havemovement data associated with it that distinguishes it from each othertype of cleaning action. Accordingly, movement data generated during theperformance of multiple cleaning actions can allow each specificcleaning action to be distinguished from each other specific cleaningaction. The specific features of the data defining a specific cleaningaction may vary, e.g., depending on the type of cleaning actionperformed and the characteristics of the sensor data generated bywearable computing device 12. Cleaning action comparison data store 32may contain data distinguishing cleaning movement from non-cleaningmovement. Cleaning action comparison data store 32 may further containdata corresponding to each type of cleaning action, the compliance ofwhich is intended to be evaluated. For example, cleaning actioncompliance data store 32 may contain features generated from referencemovement data associated with each type of cleaning action that may bedetermined from movement data.

Cleaning efficacy determination module 26 can analyze one or morefeatures of movement generated during the course of movement relative tothe features defining different cleaning actions. For example, cleaningefficacy determination module 26 can analyze one or more features ofmovement data generated during the duration of movement (e.g., cleaningevent) to distinguish periods of movement corresponding to cleaningaction from periods of movement corresponding to non-cleaning actions,e.g., with reference to cleaning action compliance data store 32.Additionally or alternatively, cleaning efficacy determination module 26can analyze one or more features of movement corresponding to periods ofcleaning to determine specific types of cleaning actions performedduring each period of cleaning, e.g., with reference to cleaning actioncompliance data store 32. Cleaning action compliance data store 32 mayfurther determine whether one or more of the specific types of cleaningactions performed were performed with a threshold level of quality,e.g., with reference to clean quality comparison data store 30.

In some examples, cleaning efficacy determination module 26 can analyzeone or more features of movement data generated during the duration ofmovement to distinguish periods of movement corresponding to cleaningaction from periods of movement corresponding to non-cleaning actions,e.g., with reference to cleaning action compliance data store 32.Cleaning efficacy determination module 26 can further analyze the one ormore features of movement data, e.g., with reference to cleaning actioncompliance data store 32, to determine whether a specified order ofcleaning was performed (e.g., cleaning room from top-to-bottom,wet-to-dry, and/or least-to-most soiled). Additionally or alternatively,cleaning efficacy determination module 26 can further analyze the one ormore features of movement data, e.g., with reference to cleaning actioncompliance data store 32, to determine whether a particular surface hasbeen cleaned used a specified technique or specified series oftechniques (e.g., a scrubbing action, using overlapping strokes,removing visible soils followed by disinfection).

Remote computing system 14 may communicate with wearable computingdevice 12 to initiate an operation via the wearable computing device inthe event that the cleaning activity performed does not comply withprotocol standards, such as a specific type of cleaning action expectedto be performed having not been performed and/or a specific type ofcleaning action having been performed to less than a threshold level ofcleaning quality.

In some examples, wearable computing device 12 may output, fortransmission to remote computing system 6, information comprising anindication of movement (e.g., data indicative of a direction, speed,location, orientation, position, elevation, etc.) of wearable computingdevice 12. Responsive to outputting the information comprising theindication of movement, wearable computing device 12 may receive, fromremote computing system 14, information concerning an efficacy ofcleaning that is being performed or has been performed. The informationmay indicate that the individual performing cleaning and wearingwearable computing device 12 has performed a cleaning operation on allsurfaces targeted for cleaning or, conversely, has not performed acleaning operation on at least one surface targeted for cleaning.Additionally or alternatively, the information may indicate that theindividual performing cleaning and wearing wearable computing device 12has performed cleaning to a threshold level of quality or, conversely,has not performed cleaning to a threshold level of quality. As still afurther example, the information may indicate that the individualperforming cleaning and wearing wearable computing device 12 has notperformed a specific type of cleaning action expected to be performed aspart of a stored cleaning protocol and/or the individual has performedthe specific type of cleaning action but has not performed it to thethreshold level of quality and/or in the wrong order.

In the example of FIG. 1, wearable computing device 12 is illustrated asa wrist-mounted device, such as a watch or activity tracker. Wearablecomputing device 12 can be implemented using a variety of differenthardware devices, as discussed above. Independent of the specific typeof device used as wearable computing device 12, the device may beconfigured with a variety of features and functionalities.

In the example of FIG. 1, wearable computing device 12 is illustrated asincluding a user interface 40. User interface 40 of wearable computingdevice 12 may function as an input device for wearable computing device12 and as an output device. User interface 40 may be implemented usingvarious technologies. For instance, user interface 40 may function as aninput device using a microphone and as an output device using a speakerto provide an audio-based user interface. User interface 40 may functionas an input device using a presence-sensitive input display, such as aresistive touchscreen, a surface acoustic wave touchscreen, a capacitivetouchscreen, a projective capacitance touchscreen, a pressure sensitivescreen, an acoustic pulse recognition touchscreen, or anotherpresence-sensitive display technology. User interface 40 may function asan output (e.g., display) device using any one or more display devices,such as a liquid crystal display (LCD), dot matrix display, lightemitting diode (LED) display, organic light-emitting diode (OLED)display, e-ink, or similar monochrome or color display capable ofoutputting visible information to the user of wearable computing device12.

User interface 40 of wearable computing device 12 may includephysically-depressible buttons and/or a presence-sensitive display thatmay receive tactile input from a user of wearable computing device 12.User interface 40 may receive indications of the tactile input bydetecting one or more gestures from a user of wearable computing device12 (e.g., the user touching or pointing to one or more locations of userinterface 40 with a finger or a stylus pen). User interface 40 maypresent output to a user, for instance at a presence-sensitive display.User interface 40 may present the output as a graphical user interfacewhich may be associated with functionality provided by wearablecomputing device 12. For example, user interface 40 may present varioususer interfaces of applications executing at or accessible by wearablecomputing device 12 (e.g., an electronic message application, anInternet browser application, etc.). A user may interact with arespective user interface of an application to cause wearable computingdevice 12 to perform operations relating to a function. Additionally oralternatively, user interface 40 may present tactile feedback, e.g.,through a haptic generator.

FIG. 1 shows that wearable computing device 12 includes one or moresensor devices 42 (also referred to herein as “sensor 42”) forgenerating data corresponding to movement of the device inthree-dimensional space. Many examples of sensor devices 42 existincluding microphones, cameras, accelerometers, gyroscopes,magnetometers, thermometers, galvanic skin response sensors, pressuresensors, barometers, ambient light sensors, heart rate monitors,altimeters, and the like. In some examples, wearable computing device 12may include a global positioning system (GPS) radio for receiving GPSsignals (e.g., from a GPS satellite) having location and sensor datacorresponding to the current location of wearable computing device 12 aspart of the one or more sensor devices 42. Sensor 42 may generate dataindicative of movement of wearable computing device in one or moredimensions and output the movement data to one or more modules ofwearable computing device 12, such as module 44. In someimplementations, sensor device 42 is implemented using a 3-axisaccelerometer. Additionally or alternatively, sensor device 42 may beimplemented using a 3-axis gyroscope.

Wearable computing device 12 may include a user interface module 44 and,optionally, additional modules (e.g., cleaning efficacy determinationmodule 26). Each module may perform operations described using software,hardware, firmware, or a mixture of hardware, software, and firmwareresiding in and/or executing at wearable computing device 12. Wearablecomputing device 12 may execute each module with one or multipleprocessors. Wearable computing device 12 may execute each module as avirtual machine executing on underlying hardware. Each module mayexecute as one or more services of an operating system and/or acomputing platform. Each module may execute as one or more remotecomputing services, such as one or more services provided by a cloudand/or cluster-based computing system. Each module may execute as one ormore executable programs at an application layer of a computingplatform.

User interface module 44 may function as a main control module ofwearable computing device 12 by not only providing user interfacefunctionality associated with wearable computing device 12, but also byacting as an intermediary between other modules (e.g., module 46) ofwearable computing device 12 and other components (e.g., user interface40, sensor device 42), as well as remote computing system 14 and/ornetwork 16. By acting as an intermediary or control module on behalf ofwearable computing device 12, user interface module 44 may ensure thatwearable computing device 12 provides stable and expected functionalityto a user. User interface module 44 may rely on machine learning orother type of rules based or probabilistic artificial intelligencetechniques to control how wearable computing device 12 operates.

User interface module 44 may cause user interface 40 to perform one ormore operations, e.g., in response to one or more cleaningdeterminations made by cleaning efficacy determination module 26. Forexample, user interface module 44 may cause user interface 40 to presentaudio (e.g., sounds), graphics, or other types of output (e.g., hapticfeedback, etc.) associated with a user interface. The output may beresponsive to one or more cleaning determinations made and, in someexamples, may provide cleaning information to the wearer of wearablecomputing device 12 to correct cleaning behavior determined to benoncompliant.

For example, user interface module 44 may receive information vianetwork 16 from cleaning efficacy determination module 26 that causesuser interface module 44 to control user interface 40 to outputinformation to the wearer of wearable computing device 12. For instance,when cleaning efficacy determination module 26 determines whether or notthe user has performed certain compliant cleaning behavior (e.g.,performed a cleaning operation on each surface targeted for cleaning,cleaned a target surface to a threshold quality of cleaning, and/orperformed a specific type of cleaning action and/or perform such actionto a threshold quality of cleaning), user interface module 44 mayreceive information via network 16 corresponding to the determinationmade by cleaning efficacy determination module 26. Responsive todetermining that wearable computing device 12 has or has not performedcertain compliant cleaning behavior, user interface module 44 maycontrol wearable computing device 12 to perform an operation, examplesof which are discussed in greater detail below.

Cleaning efficacy information determined by system 10 may be used in avariety of different ways. As noted, the cleaning efficacy informationcan be stored for a cleaning event, providing cleaning validationinformation for the environment being cleaned. Additionally oralternatively, the cleaning efficacy information can be communicated toa scheduling module, e.g., executing on system 10 or another computingsystem, which schedules the availability of certain resources in theenvironment in which the cleaning operation is being performed. In ahealthcare environment, for example, the scheduling module may determinethe availability of a room (e.g., patient room, surgical room) andschedule patient assignments/procedures for the room based on when theroom is turned over from a prior use (e.g., cleaned) and available. Asanother example, the scheduling module may determine the availability ofequipment for use based on when the equipment is turned over from aprior use (e.g., cleaned) and available. Cleaning efficacy informationdetermined by system 10 can be communicated to the scheduling module todetermine when a resource (e.g., room, equipment) is projected to becleaned and/or cleaning is complete. For example, the scheduling modulemay determine that a resource is projected to be available in a certainperiod of time (e.g., X minutes) based on substantially real-timecleaning efficacy and progress information generated by system 10. Thescheduling module can then schedule a subsequent use of the resourcebased on this information.

As another example, cleaning efficacy information determined by system10 may be used to train and/or incentivize a cleaner using the system.Computing system 10 may include or communicate with an incentive systemthat issues one or more incentives to a cleaner using the system basedon cleaning performance monitored by wearable computing device 12. Theincentive system may issue a commendation (e.g., an encouraging messageissued via user interface 40 and/or via e-mail and/or textual message)and/or rewards (e.g., monetary rewards, prizes) in response to anindividual user meeting one or more goals (e.g., efficiency goals,quality goals) as determined based on motion data generated by thewearable computing device worn by the user.

FIG. 2 is a block diagram illustrating an example wearable computingdevice configured according to one or more aspects of the presentdisclosure. For example, the wearable computing device of FIG. 2 can beconfigured to determine whether or not a wearer of the device hasperformed certain compliant cleaning behavior (e.g., performed acleaning operation on each surface targeted for cleaning, cleaned atarget surface to a threshold quality of cleaning, and/or performed aspecific type of cleaning action and/or performed such action to athreshold quality of cleaning and/or in a target cleaning order).Wearable computing device 12 of FIG. 2 is described below within thecontext of system 10 of FIG. 1. FIG. 2 illustrates only one particularexample of wearable computing device 12 of system 10, and many otherexamples of wearable computing device 12 may be used in other instancesand may include a subset of the components, additional components, ordifferent components than those included in the example wearablecomputing device 12 shown in FIG. 2.

As shown in the example of FIG. 2, wearable computing device 12 includesuser interface 40, sensor device 42, one or more processors 50, one ormore input devices 52, one or more communication units 54, one or moreoutput devices 56, and one or more storage devices 58. Storage devices48 of wearable computing device 12 also include user interface module44, cleaning efficacy determination module 60, application modules62A-62Z (collectively referred to as, “application modules 62”), anddata stores 64, 66, and 68.

Cleaning efficacy determination module 60 may generally correspond tocleaning efficacy determination module 26 of remote computing system 14of system 10. Data stores 64, 66, and 68 may correspond, respectively,to data stores 28, 30, and 32 of remote computing system 14 of FIG. 1.Accordingly, functions described as being performed by or on remotecomputing system 14 (in combination with functions performed on wearablecomputing device 12) may be performed solely on wearable computingdevice 12 and/or processing tasks may otherwise be shifted from remotecomputing system 14 to wearable computing device 12.

Communication channels 70 may interconnect each of the components ofwearable computing device 12 for inter-component communications(physically, communicatively, and/or operatively). In some examples,communication channels 70 may include a system bus, a networkconnection, an inter-process communication data structure, or any othermethod for communicating data.

One or more input devices 52 of wearable computing device 12 may receiveinput. Examples of input are tactile, audio, and video input. Inputdevices 52 of wearable computing device 12, in one example, includes apresence-sensitive display, touch-sensitive screen, mouse, keyboard,voice responsive system, video camera, microphone or any other type ofdevice for detecting input from a human or machine. One or more outputdevices 56 of wearable computing device 12 may generate output. Examplesof output are tactile, audio, and video output. Output devices 56 ofwearable computing device 12, in one example, includes a hapticgenerator that provides tactile feedback to the wearer.

One or more communication units 54 of wearable computing device 12 maycommunicate with external devices (e.g., remote computing system 14) viaone or more networks by transmitting and/or receiving network signals onthe one or more networks. For example, wearable computing device 12 mayuse communication unit 54 to send and receive data to and from remotecomputing system 14 of FIG. 1. Wearable computing device 12 may usecommunication unit 54 to transmit and/or receive radio signals on aradio network such as a cellular radio network. Likewise, communicationunits 54 may transmit and/or receive satellite signals on a satellitenetwork such as a global positioning system (GPS) network. Examples ofcommunication unit 54 include a network interface card (e.g. such as anEthernet card), an optical transceiver, a radio frequency transceiver, aGPS receiver, or any other type of device that can send and/or receiveinformation. Other examples of communication units 54 may include shortwave radios, cellular data radios, wireless Ethernet network radios, aswell as universal serial bus (USB) controllers.

In some examples, user interface 40 of wearable computing device 12 mayinclude functionality of input devices 52 and/or output devices 56.While illustrated as an internal component of wearable computing device12, user interface 40 also represents and external component that sharesa data path with wearable computing device 12 for transmitting and/orreceiving input and output. For instance, in one example, user interface40 represents a built-in component of wearable computing device 12located within and physically connected to the external packaging ofwearable computing device 12 (e.g., a screen on a mobile phone). Inanother example, user interface 40 represents an external component ofwearable computing device 12 located outside and physically separatedfrom the packaging of wearable computing device 12 (e.g., a device thatshares a wired and/or wireless data path with the other components ofwearable computing device 12).

One or more storage devices 64-68 within wearable computing device 12may store information for processing during operation of wearablecomputing device 12 (e.g., wearable computing device 12 may store targetsurface comparison data 64 corresponding to data store 28 in FIG. 1),cleaning quality comparison data 66 (corresponding to data store 30 inFIG. 1), and/or cleaning action comparison data 68 (corresponding todata store 32 in FIG. 1). Such data may be accessed by other modules andfeatures of wearable computing device 12 during execution at wearablecomputing device 12. In some examples, storage device 58 is a temporarymemory, meaning that a primary purpose of storage device 58 is notlong-term storage. Storage devices 58 on wearable computing device 12may be configured for short-term storage of information as volatilememory and therefore not retain stored contents if powered off. Examplesof volatile memories include random access memories (RAM), dynamicrandom access memories (DRAM), static random access memories (SRAM), andother forms of volatile memories known in the art.

Storage devices 58, in some examples, also include one or morecomputer-readable storage media. Storage devices 58 may be configured tostore larger amounts of information than volatile memory. Storagedevices 58 may further be configured for long-term storage ofinformation as non-volatile memory space and retain information afterpower on/off cycles. Examples of non-volatile memories include magnetichard discs, optical discs, floppy discs, flash memories, or forms ofelectrically programmable memories (EPROM) or electrically erasable andprogrammable (EEPROM) memories. Storage devices 58 may store programinstructions and/or data for performing the features and functionsdescribed herein as being performed by any module, device, and/orsystem.

One or more processors 50 may implement functionality and/or executeinstructions within wearable computing device 12. For example,processors 50 on wearable computing device 12 may receive and executeinstructions stored by storage devices 58 that execute the functionalityof user interface module 44, cleaning efficacy determination module 60,and application modules 62. These instructions executed by processors 50may cause wearable computing device 12 to store information, withinstorage devices 58 during program execution. Processors 50 may executeinstructions of modules (e.g., 44, 60, 62) to cause wearable computingdevice 12 to determine compliance with one or more characteristics ofcleaning and, in some examples, control execution of an operation inresponse to determining one or more non-compliant behaviors. Forexample, processors 50 may execute instructions that cause userinterface 40 to output at least one of an audible type alert, a visualtype alert, and/or a haptic feedback type alert. Such one or more alertsmay provide information indicating non-compliance with a cleaningprotocol (e.g., failure to clean all surfaces targeted for cleaning,failure to clean a particular surface to a threshold cleaning quality),instruct the user on behavior to correct the non-compliance, specifydetails of the non-compliant activity (e.g., identify the missed targetsurface(s)), and/or otherwise inform the user of that the cleaningperformed did not satisfy compliance standards.

Application modules 62 may include any additional type of applicationthat wearable computing device 12 may execute. Application modules 62may be stand-alone applications or processes. In some examples,applications modules 62 represent an operating system or computingplatform of wearable computing device 12 for executing or controllingfeatures and operations performed by other applications.

A variety of different surfaces and objects may be cleaned utilizing oneor more aspects of the present disclosure. Examples of such surfaces arediscussed in greater detail below in connection with FIG. 4. In someexamples, one or more surfaces on which cleaning is performed arelocated in a healthcare environment, as discussed in connection withFIG. 1. In other examples, one or more surfaces on which cleaning isperformed are located in a food preparation environment. FIGS. 3A-3Cillustrate example surfaces and/or equipment that may be cleaned,optionally using example tools, the cleaning efficacy of which isevaluated according to the present disclosure. FIG. 3A illustrates anexample floor cleaning protocol that may be specified for a wearer ofwearable computing device 12 to follow. FIG. 3B illustrates an examplegrill cleaning protocol that may be specified for a wearer of wearablecomputing device 12 to follow. FIG. 3C illustrates an example fryercleaning protocol that may be specified for a wearer of wearablecomputing device 12 to follow.

To make one or more cleaning efficacy determinations using wearablecomputing device 12, one or more calibration process may be performed togenerate comparison data stored in data stores for reference during asubsequent cleaning event. For example, a supervised process may be usedin which the individual that will wear the wearable computing deviceduring subsequent cleaning activity goes through a calibration processusing the device, or an analogue thereof (e.g., a device generatingequivalent movement data to that generated by wearable computing device12). Alternatively, a global, non-user-specific training may beperformed to generate comparison data that is subsequently referencedduring use of wearable computing device, which may help remove any needfor user-specific calibration, but which may be less accurate. Thus, insome implementations, reference movement data stored in a data store(e.g., in wearable computing device 12 and/or remote computing system14) is generated from movement data obtained during one or more trainingepisodes in which one or more trainers (different that the individualsubsequently performing cleaning) performs a cleaning operation (e.g.,on each of a plurality of target surfaces or equivalents thereof) whilewearing wearable computing device 12 or an equivalent thereof. In otherimplementations, reference movement data stored in a data store isgenerated from movement data obtained during one or more trainingepisodes in which the actual individual performing the subsequentcleaning operation (e.g., on each of the plurality of target surfaces orequivalents thereof) wears wearable computing device 12 or an equivalentthereof.

Independent of how comparison data is generated, computing system 10 maybe used to generate and/or store comparison data associated withdifferent surfaces and/or areas to be cleaned and/or different levels ofcleaning (e.g., different cleaning protocols) to be performed on thosesurfaces and/or areas. In some examples, a user may provide a user inputto computing system 10 indicating that wearable computing device 12 isto be reassigned to monitor cleaning of one or more differentsurface(s), room(s), and/or areas than the wearable computing device waspreviously used to monitor. Alternatively, computing system 10 mayautomatically determine that the wearable computing device 12 has beenreassigned based on motion data generated by the wearable computingdevice. In either case, computing system 10 may reset the context ofmeasurement and/or the comparison data against which motion datagenerated by wearable computing device 12 is compared during subsequentoperation. Additionally or alternatively, computing system 10 may changethe level of cleaning to be performed and/or the protocol against whichcleaning data is compared (e.g., such as when a hospital room isswitched from a daily maintenance cleaning to a more thorough dischargecleaning).

FIG. 4 is a flow diagram illustrating an example process for training anexample wearable computing device to subsequently determine whether anindividual performing cleaning has cleaned each of a plurality of targetsurfaces intended to be clean as part of an established protocol. Theprocess shown in FIG. 4 may be performed by one or more processors of acomputing device, such as wearable computing device 12 illustrated inFIGS. 1 and 2. For purposes of illustration, FIG. 4 is described belowwithin the context of computing system 10 of FIG. 1. It should beappreciated that the process of FIG. 4 may be performed by theindividual who will be wearing wearable computing device 12 duringsubsequent cleaning or may be performed by a different individual (e.g.,a trainer) other than the individual who will be performing thesubsequently cleaning. In some examples, the process of FIG. 4 isperformed by a single individual, while in other implementations,multiple different individuals perform the process to generate anaggregate data set corresponding to a broader population of users. Forexample, the generation of reference movement data according to any ofthe techniques described herein may be performed by: (1) a singleindividual in a single session, (2) multiple individuals in a singlesession for each, (3) a single individual across multiple sessions,and/or (4) multiple individuals each across multiple sessions.

In the example of FIG. 4, an individual wearing wearable computingdevice 12 performs a cleaning operation on each of a plurality of targetsurfaces (100). The plurality of target surfaces may be at least twosurfaces, such as at least five surfaces, or at least ten surfaces, orat least fifteen surfaces. In some implementations, each target surfaceis a target object. Accordingly, description of performing a cleaningoperation on a target surface may be implemented by performing suchcleaning operation on a target object. Each target object may haveboundaries in three-dimensional space that define an extent of theobject to be cleaned. The boundaries of each target object may bedifferent than each other target object intended to be cleaned, suchthat the cleaning operation performed for each target object results ina different movement than the cleaning operation performed for eachother target object.

Each cleaning operation may be a movement action corresponding tocleaning of the target surface or object, optionally along withpre-cleaning preparatory motion that precedes cleaning of the targetsurface or object. Each cleaning operation may be performed by theindividual wearing wearable computing device 12 with or without the aidof a tool (e.g., mop, brush, sprayer, sponge, wipe). Each cleaningoperation may involve movement of the individual's hand, arm, and/orbody in one or more dimensions. For example, a cleaning operation mayinvolve a horizontal, vertical, and/or rotational movement of the handcorresponding to cleaning whereby force is transferred from theindividual's hand to the target surface being cleaned, e.g., via acleaning tool. For example, one type of cleaning operation that may beperformed is a wiping cleaning movement in which the individual movestheir body to wipe a target surface. Another type of cleaning operationthat may be performed is a floor cleaning operation in which theindividual performs a floor sweeping or mopping motion, e.g., in whichthe individual is standing upright and conveys force through a toolextending down to the floor surface. Another example of a type ofcleaning operation that may be performed is an equipment cleaningoperation. An equipment cleaning operation may be one in which theindividual cleans equipment that is active or used during normaloperation in the environment.

The surfaces or objects targeted for cleaning may be selected accordingto a cleaning protocol specifying surfaces that should be cleaned duringa cleaning event. The specific surfaces selected for cleaning accordingto the protocol will vary depending on the application and environmentin which the cleaning protocol is executed. Example surfaces that may betargeted for cleaning (e.g., in a hotel or healthcare environment)include, but are not limited to, those that define a light switch, atable top, a bed rail, a door knob, a medication dispensing pole, atelevision remote control, and combinations thereof. Other examplesurfaces that may be targeted for cleaning (e.g., in a food preparationenvironment) include, but are not limited to, those that defineequipment used in the environment, such as a grill, a fryer, arefrigerator, a microwave, and combinations thereof.

In general, the types of surfaces targeted for cleaning may includefloor surfaces and non-floor surfaces, which may be surfaces and objectselevated above the floor in which they reside. For example, theindividual wearing wearable computing device 12 may perform a mopping, asweeping, and/or deck brushing cleaning action on a floor surface.Additionally or alternatively, the individual wearing wearable computingdevice 12 may perform non-floor surface cleaning actions, such ascleaning a sink, faucet handle, toilet, countertop, etc. andcombinations thereof. Each target surface may define an objection havingflat horizontal surfaces, flat vertical surfaces, cavities, cylinders,spheres, and combinations thereof.

The individual performing a cleaning operation on each target surfacewhile wearing wearable computing device 12 may perform the cleaningoperation according to a protocol. The protocol may specify how thecleaning operation is to be performed on each target surface, e.g., atype of cleaning tool to be used, an extent of the surface to becleaned, and/or a type and direction of force to be applied at one ormore stages of the cleaning operation. In other words, the cleaningprotocol may dictate a technique to be followed for cleaning each targetsurface, which will be followed while wearing wearable computing device12 according to the training technique of FIG. 4 and is also instructedto be followed during subsequent cleaning events.

According to the technique of FIG. 4, sensor device 42 of wearablecomputing device 12 can generate movement data associated with movementof wearable computing device 12 during the cleaning operation performedon each of the plurality of target surfaces (102). Such movement datamay be indicative of three-dimensional acceleration of wearablecomputing device 12 during the cleaning operation performed on eachtarget surface and/or indicative of three-dimensional orientation of thewearable computing device during the cleaning operation. Other sensordata that may be generated include those data discussed above, such asGPS data.

Movement data generated by sensor device 42 of wearable computing device12 during one or more training sessions can be associated with thecleaning of different target surfaces according to the technique of FIG.4 (104). For example, one or more modules (e.g., module 26) executingwithin computing system 10 may receive the data generated by sensordevice 42 and associate different portions of the movement data with aparticular one of each of the plurality of target surfaces on which theindividual wearing wearable computing device 12 has performed a cleaningoperation.

For example, movement data generated by sensor device 42 of wearablecomputing device 12 may be wirelessly transmitted via network 16 toremote computing system 14 for analysis by one or more modules executingat the remote computing system. Different portions of the movement datagenerated by sensor device 42 may be associated with a correspondingtarget surface in a number of different ways. As one example, anindividual associated with the training event may inform remotecomputing system 14 (e.g., via user interface 40) when a cleaningoperation is being performed on a target surface (e.g., by indicating astart and a stop of the cleaning operation). In other words, anindividual associated with the training event may assign cleaning ofeach target surface to a corresponding portion of movement data,allowing remote computing system 14 to associate movement data generatedduring a cleaning operation performed on a particular target surface tothat target surface.

As another example, a communication unit associated with a tool used toclean a target surface and/or the target surface itself may provide anindication when that target surface is being cleaned. For example,wearable computing device 12 may receive communication signals from atool associated with cleaning a particular target surface and/or acommunication unit associate with the target surface target surfaceitself (e.g., near-field-communication radio, Wi-Fi radio, CB radio,Bluetooth radio, etc.), thereby indicating when that target surface isbeing cleaned. Remote computing system 14 can receive data correspondingto a time when the particular target surface is being cleaned (e.g.,corresponding to the signal provided by the cleaning tool associate withthe target surface and/or target surface emitter) and associate movementdata corresponding with the cleaning operation on the target surfacewith that target surface.

Independent of the specific technique used to associate differentportions of movement data generated during cleaning, the exampletechnique of FIG. 4 includes determining reference data indicative of acleaning operation being performed on a target surface for each of theplurality of different target surfaces (106). For example, a moduleexecuting on remote computing system 14 (e.g., a feature generationmodule) can process the movement data associated with each targetsurface to generate one or more features from the movement dataindicative of a cleaning operation performed on that target surface. Themovement data associated with a cleaning operation performed on eachtarget surface can be filtered using a time-domain feature window and/ora frequency-domain window having a set duration (e.g., 1 second), withshorter duration windows providing more granularity. By contrast, longerduration windows provide reduced processing requirements and afford theopportunity for more cycles of cleaning motions (e.g., wiping,scrubbings, mop strokes) to manifest in the frequency domain. Candidatefeatures for characterizing the movement data can be stored in a datastore associated with remote computing system 14 and applied to thegenerate movement data. Each candidate feature may correspond todifferent aspects of a kinetic motion that makes up a cleaning operationassociated with a particular target surface.

Candidate features can be generated for different aspects of themovement sensor data generated by sensor device 42 and/or differentdomains of the data. For example, when sensor device 42 is configured togenerate inertial movement data (e.g., acceleration data, gyroscopedata) across one or more axes, uniaxial and/or multiaxial features maybe generated for the sensor data. Single axial features aretransformation of a single inertial measurement unit (IMU) axis (e.g.,acceleration or gyroscope reading in the x, y, or z axis). Sensitivityfeatures can also be multiaxial features, which are transformations ofmultiple IMU axes at a given time point.

Additionally or alternatively, candidate features can be generated fordifferent domains of the movement sensor data generated by sensor device42. For example, time-domain features can be generated by applyingtransformations to each time-domain window of the sensor data. Asanother example, frequency-domain Fourier features can be generated byapplying transformations to spectra arising from a discrete Fouriertransform of the sensor data in each frequency domain window. As afurther example, wavelet features can be generated by appliedtransformations to the spectra arising from a discrete wavelet transformof the sensor data in each frequency domain window.

One example class of features that may be generated are uniaxialtime-domain features. Base functions that can be applied to the eachtime-domain window for each single axis IMU data—for exampleacceleration sensor data (e.g., x, y, and/or z) and/or gyroscope sensordata (e.g., x, y, and/or z)—include, but are not limited to: mean,median, variance, standard deviation, maximum, minimum, window range,root mean square (RMS), univariate signal magnitude area (SMA),zero-crossings, mean absolute jerk, standard deviation of absolute jerk,univariate SMA jerk, and combinations thereof.

Another example class of features that may be generated are multiaxialtime-domain features. Base functions that can be applied to the eachtime-domain window across multiple axes of sensor data include, but arenot limited to: xy-correlation, yz-correlation, xz-correlation, sum ofsignal magnitude area, mean of signal vector magnitude, standarddeviation of signal vector magnitude, maximum xy-difference, maximumyz-difference, maximum xz-difference, and combinations thereof.

A further example class of features that may be generated areuniaxial-frequency-domain Fourier features. Base functions that can beapplied to the spectra arising from the domain Fourier transform of thetime domain signal in each frequency-domain window include, but are notlimited to: DC offset, peak frequencies (e.g., top 3), peak amplitudes(e.g., top 3), and spectral energy.

A further example class of features that may be generated are uniaxialwavelet features. Base functions that can be applied to the spectraarising from the discrete wavelet transformation of the time domainsignal in each frequency-domain window include, but are not limited to:wavelet persistence (e.g., in low, low-mid, mid-high, and high bands)and spectral energy (e.g., in low, low-mid, high-mid, and high bands).

Any or all candidate features can be generated for each duration segmentof data generated by sensor device 42 of wearable computing device 12and being associated with cleaning of a particular target surface. Thefeatures so generated can form a feature vector for each duration ortime window of motion analyzed, with the time series of all such vectorsforming a feature matrix from which feature selection is performed.

After generating a plurality of candidate features for characterizingthe movement data associated with a cleaning operation performed in eachtarget surface, one or more specific candidate features can be selectedto define the reference movement data used in subsequent analysis andcharacterization of movement data during a cleaning event. Specificfeatures can be selected from the pool of candidate features (e.g., by amodule executing on remote computing system 14) based on theseparability of the features in space. That is, features that adequatelyor best distinguish the desired reference data, for example cleaning ofone target surface compared to different target surface target surface,can be selected.

Candidate features can be generated and selected using any type ofsupervised learning algorithm. Example supervised learning algorithmsthat can be used include, but are not limited to, a Bayesian network, aneural network, a k-nearest neighbor, a random forest, a support vectormachine, and/or combinations of supervised learning algorithms, referredto as ensemble classifiers.

In the technique of FIG. 4, reference data for each target surface onwhich a cleaning operation was performed for characterization can bestored (e.g., in a data store 64 on wearable computing device 12 and/ora data store 28 on remote computing system 14) (108). Reference movementdata may be stored in the form of raw data. Additionally oralternatively, reference movement data may be stored in the form of afeature set identified through the feature selection process discussedabove that discriminates movement associated with a cleaning operationbeing performed on one target surface from movement associated with acleaning operation being performed on each other target surface. Thus,it should be appreciated that discussion of reference movement data doesnot mean that the raw reference movement data need be used in subsequentanalyses but rather data derived from or based on the raw referencemovement data may be used. In any case, reference data associated with acleaning operation being performed in each of the plurality of targetsurfaces may be stored for use in connection with the evaluation and/orcharacterization of subsequent cleaning events.

FIG. 5 is a flow diagram illustrating an example process for training anexample wearable computing device to subsequently determine whether anindividual performing cleaning has effectively cleaned the targetsurface to a threshold quality of cleaning. The process shown in FIG. 5may be performed by one or more processors of a computing device, suchas wearable computing device 12 illustrated in FIGS. 1 and 2. Forpurposes of illustration, FIG. 5 is also described below within thecontext of computing system 10 of FIG. 1. It should be appreciated thatthe process of FIG. 5 may be performed by the individual who will bewearing wearable computing device 12 during subsequent cleaning or maybe performed by a different individual (e.g., a trainer) other than theindividual who will be performing the subsequently cleaning, asdiscussed above with respect to FIG. 4.

In the example of FIG. 5, an individual wearing wearable computingdevice 12 performs a cleaning operation on one or more target surfaces,the quality of cleaning of which is intended to be characterized duringa subsequent cleaning event (120). The target surface may be any ofthose surfaces or objects discussed herein, including with respect toFIG. 4 above. Each target surface may define an object having boundariesin three-dimensional space that define an extent of the object to becleaned.

The individual performing cleaning while wearing wearable computingdevice 12 may perform a cleaning operation on the target surfaceaccording to a protocol. The protocol may define a threshold quality ofcleaning for the surface. For example, the protocol may be establishedsuch that compliance with the protocol indicates that the surface isclean to a threshold quality of cleaning whereas noncompliance with theprotocol indicates that the surface is not cleaned to the thresholdquality of cleaning.

The protocol may specify how the cleaning operation is to be performedon the target surface, e.g., a type of cleaning tool to be used, anextent of the surface to be cleaned, and/or a type and direction offorce to be applied at one or more stages of the cleaning operation. Forexample, the cleaning protocol may dictate a technique to be followedfor cleaning the target surface which, if followed while wearingwearable computing device 12 during a subsequent cleaning event, willindicate that the surface is clean to a threshold quality of cleaning.The protocol may be developed by cleaning specialist with knowledge ofcleaning characteristics of different surfaces, pathogen killed times,and other experiential or laboratory data guiding development of aprotocol to achieve a threshold quality of cleaning.

According to the technique of FIG. 5, sensor device 42 of wearablecomputing device 12 can generate movement data associated with movementof wearable computing device 12 during the cleaning operation performedon the target surface (122). In some examples, one or more modulesexecuting within remote computing system 14 may receive the datagenerated by sensor device 42 for further processing. For example,movement data generated by sensor device 42 of wearable computing device12 may be wirelessly transmitted via network 16 to remote computingsystem 14 for analysis by one or more modules executing at the remotecomputing system. Where the movement data generated by sensor device 42includes movement data other than that associated with cleaning of thetarget object according to the protocol to establish the thresholdquality of cleaning, a portion of the movement data generated by sensordevice 42 corresponding to the cleaning can be associated with thecleaning, e.g., as discussed above with respect to FIG. 4.

The example technique of FIG. 5 includes determining reference dataindicative of a threshold quality of cleaning performed on a targetsurface (124). For example, a module executing on remote computingsystem 14 (e.g., a feature generation module) can process the movementdata associated with cleaning of the target surface to generatecharacteristics of the reference data. For example, raw movement datacan be processed to generate a plurality of candidate features forcharacterizing the movement data associated with the quality of cleaningperformed on the target surface, e.g., following the feature generationtechniques discussed above with respect to FIG. 4. One or more specificcandidate features can then be selected to define the reference movementdata used in subsequent analysis and characterization of movement datagenerated during cleaning to characterize the quality of clean of thetarget surface, e.g., following the feature selection techniquesdiscussed above with respect to FIG. 4.

Reference data generated for a target surface corresponding to a qualityof cleaning of the target surface can be stored (e.g., in a data store66 on wearable computing device 12 and/or a data store 30 on remotecomputing system 14) (126). Reference movement data may be stored in theform of raw data. Additionally or alternatively, reference movement datamay be stored in the form of a feature set identified through thefeature selection process that discriminates movement associated with aquality of cleaning performed on a target surface. Independent of theformat of the data, reference data associated with a quality of cleanperformed on a surface may be stored for use in connection with theevaluation and/or characterization of a subsequent cleaning event.

FIG. 6 is a flow diagram illustrating an example process for training anexample wearable computing device to subsequently evaluate a pluralityof different cleaning actions, e.g., as part of a total hygienemanagement system. The multiple different cleaning actions may includeat least two different types of cleaning actions, such as three or morecleaning actions. Example cleaning actions that may be performed includefloor surface cleaning actions, equipment cleaning actions, and handhygiene cleaning actions. Other types of cleaning actions that may beperformed include non-floor surface and non-equipment cleaning actions,such as cleaning actions performed on elevated surfaces (e.g., toilets,doorknobs, counters, and other surfaces such as those discussed above).The process shown in FIG. 6 may be performed by one or more processorsof a computing device, such as wearable computing device 12 illustratedin FIGS. 1 and 2. For purposes of illustration, FIG. 6 is also describedbelow within the context of computing system 10 of FIG. 1. It should beappreciated that the process of FIG. 6 may be performed by theindividual who will be wearing wearable computing device 12 duringsubsequent cleaning or may be performed by a different individual (e.g.,a trainer) other than the individual who will be performing thesubsequently cleaning, as discussed above with respect to FIG. 4.

In the example of FIG. 6, an individual wearing wearable computingdevice 12 performs multiple different cleaning actions, each of whichmay be performed during a subsequent cleaning event (130). The targetcleaning actions may include a non-hand hygiene cleaning actionperformed on any surface or object discussed herein, including withrespect to FIG. 4 above. Each target surface may define an object havingboundaries in three-dimensional space that define an extent of theobject to be cleaned. The target cleaning actions may also include ahand hygiene cleaning action in which the wearer of the wearablecomputing device 12 cleans their hands.

The individual performing cleaning while wearing wearable computingdevice 12 may perform each cleaning action according to a correspondingprotocol. For the non-hand hygiene cleaning actions, the protocol mayspecify how a cleaning operation is to be performed on a target surface,e.g., as discussed above with respect to FIGS. 3A-3C, 4, and 5. For thehand hygiene cleaning action, a corresponding hand hygiene cleanprotocol may be used. FIG. 7 illustrates an example hand hygieneprotocol that may be specified for wearer of wearable computing device12 to follow, although other protocols can be used.

According to the technique of FIG. 6, sensor device 42 of wearablecomputing device 12 can generate movement data associated with movementof wearable computing device 12 during each cleaning action performedand, optionally, between cleaning actions when non-cleaning actions arebeing performed (132). In some examples, one or more modules executingwithin remote computing system 14 may receive the data generated bysensor device 42 for further processing. For example, movement datagenerated by sensor device 42 of wearable computing device 12 may bewirelessly transmitted via network 16 to remote computing system 14 foranalysis by one or more modules executing at the remote computingsystem. Where the movement data generated by sensor device 42 includesmovement data multiple cleaning actions, a portion of the movement datagenerated by sensor device 42 corresponding to each cleaning action canbe associated with that cleaning action, e.g., as discussed above withrespect to FIG. 4.

The example technique of FIG. 6 includes determining reference dataindicative of each type of cleaning action performed in whichdistinguishes each specific type of cleaning action from each other typeof cleaning action (134). For example, a module executing on remotecomputing system 14 (e.g., a feature generation module) can process themovement data associated with each cleaning action to generatecharacteristics of the reference data. For example, raw movement datacan be processed to generate a plurality of candidate features forcharacterizing the movement data associated with each cleaning action,e.g., following the feature generation techniques discussed above withrespect to FIG. 4. One or more specific candidate features can then beselected to define the reference movement data used in subsequentanalysis and characterization of movement data generated during theperformance of multiple cleaning actions, e.g., following the featureselection techniques discussed above with respect to FIG. 4.

Reference data generated for each type of cleaning action can be stored(e.g., in a data store 68 on wearable computing device 12 and/or a datastore 32 on remote computing system 14) (136). Reference movement datamay be stored in the form of raw data. Additionally or alternatively,reference movement data may be stored in the form of a feature setidentified through the feature selection process that discriminatesmovement associated with one specific type of cleaning action from eachother specific type of cleaning action. Independent of the format of thedata, reference data associated with each specific type of cleaningaction may be stored for use in connection with the evaluation and/orcharacterization of a subsequent cleaning event.

The example calibration techniques described above with respect to FIGS.4-6 may be performed on generic surfaces of similar character but havingdifferent dimensions than those surfaces actually cleaned in subsequentuse. For example, one or more training sessions may be performed duringwhich a representative substitute for the target surface to be cleanedis cleaned. As one example, a cleaning operation may be performed on ageneric sink different than the actual sink to be cleaned duringsubsequent use. Data generated by cleaning the generic sink may bestored as reference movement data associated with cleaning of a sink andused to subsequently characterize the clean of the actual sink. The useof generic substitutes for the actual surfaces intended to be cleanedduring subsequent use can facilitate the development of global, ornon-customer-specific reference movement data sets.

In other implementations, the example calibration techniques describedabove with respect to FIGS. 4-6 may be performed on the actual surface(or a substantially exact replica thereof) to be cleaned in subsequentuse. For example, one or more of the described calibration techniquesmay be performed in the environment in which cleaning efficacy isintended to be subsequently evaluated and on the actual target surface(or a substantially exact replica thereof) to generate more accuratereference movement data.

In subsequent use, cleaning efficacy determination module 26 can analyzemovement data generated during a cleaning event with reference tocomparative data stored in one or more data stores. Cleaning efficacydetermination module 26 may determine if movement during the cleaningevent is associated with cleaning action or non-cleaning action and/ordetermine whether movement during the cleaning event indicates that acleaning action is in compliance with one or more standards.

In practice, certain cleaning events may deviate from a typical orplanned course of cleaning. For example, a cleaning event may deviatefrom a planned course of cleaning where an area to be cleaned issignificantly more soiled than is typically expected. This maynecessitate extra cleaning on one or more surfaces beyond what acleaning protocol for the surface(s) would otherwise specify. A heavilysoiled area may also necessitate cleaning one or more surfaces that arenot otherwise specified to be cleaned as part of a cleaning protocol. Asanother example, a cleaning event may be interrupted such that theindividual performing cleaning does not complete a cleaning protocol.This may occur, for example, if the individual performing cleaning isreassigned to perform an alternative task during a cleaning event or ifexternal conditions require termination of the cleaning event (e.g., anurgent patient need is identified by a cleaner performing maintenancecleaning of a patient's room in a healthcare environment).

User interface 40 of wearable computing device 12 may be configured toallow an individual associated with the wearable computing device toindicate when a cleaning event deviates from an expected cleaningprotocol, e.g., because the cleaning protocol was not completed. Userinterface 40 may have include a physically-depressible button and/or mayreceive one or more gestures from a user of wearable computing device 12(e.g., the user touching or pointing to one or more locations of theuser interface) to indicate that the cleaning event is deviating from anexcepted course of action such that a planned cleaning protocol is notexecuted as specified by the protocol.

A variety of different actions may be performed in response to a userinput indicating that cleaning is deviating from a planned cleaningprotocol. As one example, movement data associated with the cleaningevent may be designated as deviating from the expected cleaningprotocol. Movement data so designated may be filtered or otherwiseseparately treated from other movement data during one or move cleaningevents not designated via user interface 40 as deviating from anexpected protocol. This may allow more accurate cleaning validationinformation to be generated, displayed, and/or stored by separatingabnormal cleaning events from standard cleaning events. Additionally oralternatively, the number and frequency of cleaning events designated asdeviating from an expected protocol may be tracked and compared, e.g.,to a threshold value and/or between different cleaners. This may provideinsights into which cleaner(s) are experiencing more cleaning eventsdesignated as deviating from an expected protocol than other cleaners,potentially indicating supplemental training for the cleaner, changes toa particular cleaning protocol, and/or environmental changes to reducethe number of cleaning events designated as exceptional.

FIG. 8 is a flowchart illustrating an example operation of an examplewearable computing device configured to track cleaning efficacy forreducing illnesses and infections caused by ineffective cleaning inaccordance with one or more aspects of the present disclosure. Thetechnique shown in FIG. 8 may be performed by one or more processors ofa computing device, such as wearable computing device 12 and/or remotecomputing system 14.

In the example technique of FIG. 8, wearable computing device 12 candetect movement associated with the device during a cleaning event(150). The movement may be generated by an individual performingcleaning during the cleaning event, with multiple target surfacesintended to be cleaned during the event. Wearable computing device 12may detect movement via sensor device 42 and generate movement datacorresponding to the movement.

The plurality of surfaces targeted for cleaning during the cleaningevent may be any surfaces and objects discussed herein, including thosediscussed with respect to FIG. 4. The individual performing cleaningduring the cleaning event may be instructed to clean each of theplurality of target surfaces following a cleaning protocol, e.g., whichmay be the same protocol used to generate reference movement datacorresponding to a cleaning operation being performed on each targetsurface.

At least one sensor of wearable computing device 12 may generatemovement data corresponding to movement during a cleaning operation. Oneor more processors of wearable computing device 12 may receive thegenerated movement data and control transmission of the movement data,or data derived therefrom, to remote computing system 14. One or moreprocessors 50 executing on remote computing system 14 may receive thedata and execute instructions that cause cleaning efficacy determinationmodule 26 to evaluate an efficacy of the cleaning performed.

Cleaning efficacy determination module 26 executing on remote computingsystem 14 may determine whether the individual performing cleaning hasperformed a cleaning operation on each of the plurality of surfacestargeted for cleaning (152). Cleaning efficacy determination module 26can compare movement data generated by sensor device 42 of wearablecomputing device 12 during the cleaning event with reference movementdata associated with cleaning of each of the plurality of targetsurfaces in data store 28 to make such determination. For example,cleaning efficacy determination model 26 may compare movement datagenerated throughout the duration of the cleaning event with referencemovement data associated with each of the plurality of target surfaces,e.g., to determine if movement data generated at any period of timeduring the cleaning event corresponded to each of the plurality oftarget surfaces. If movement data generated during the cleaning event isnot determined to be associated with reference data associate with atleast one target surface, cleaning efficacy determination module 26 maydetermine that a cleaning operation was not performed on the targetsurface(s) during the cleaning event.

In some implementations, cleaning efficacy determination module 26determines at least one signal feature for the received movement data tocompare the movement data generated during the cleaning event to thereference movement data. For example, cleaning efficacy determinationmodule 26 may determine a plurality of signal features for the receivedmovement data generated by sensor device 42 during the cleaning event.The one or more signal features generated for the received movement datamay correspond to those features selected during a calibration processto distinguish a cleaning operation performed on one target surface froma cleaning operation performed on different target surface. For example,the one or more signal features may correspond to those discussed abovewith respect to FIG. 4. Cleaning efficacy determination module 26 maycompare the one or more signal features determined for the movement datagenerated during the cleaning event with reference signal feature datagenerated during calibration and stored in data store 28 correspondingto cleaning of each of the plurality of target surfaces, e.g., asdiscussed above with respect to FIG. 4.

When wearable computing device 12 is implemented with multiple sensors(e.g., including an accelerometer and a gyroscope), each of the multiplesensors may generate corresponding movement data during the cleaningevent. Cleaning efficacy determination module 26 executing on remotecomputing system 14 may determine one or more signal features based onmovement data generated by and received from each of the plurality ofsensors. For example, cleaning efficacy determination module 26 mayreceive first movement data corresponding to an acceleration of wearablecomputing device 12 and second movement data corresponding to an angularvelocity of the wearable computing device (for a gyroscope). Cleaningefficacy determination module 26 may determine at least one signalfeature based on the first movement data and at least one additionalsignal feature based on the second movement data to characterize themovement performed during the cleaning event.

Depending on the characteristics of the surfaces targeted for cleaning,the individual wearing wearable computing device 12 may perform multipledifferent types of cleaning operations. For example, one type of targetsurface may be a horizontal surface (e.g., a countertop) having ahorizontal wiping movement as a cleaning operation. Another type oftarget surface may be a vertical surface (e.g., a medication supportportal) having a vertical wiping movement as a cleaning operation. Yetanother type of target surface may be a doorknob having an arcuate shapeto be cleaned characterized by yet a different type of cleaningoperation with a rotary wiping movement. Thus, depending on the types ofsurfaces being cleaned and/or the protocol specified for cleaning eachtype of surface, the individual wearing wearable computing device 12 mayperform one or more cleaning operations during the cleaning event.

In some examples, the individual performing cleaning performs at least afirst cleaning operation for a first one of the plurality of targetsurface and a second cleaning operation different than the firstcleaning operation for a second one of the plurality of target surfaces.In some additional examples, the individual performing cleaning performsa different cleaning operation on each one of the plurality of differentsurfaces targeted for cleaning.

The technique of FIG. 8 includes wearable computing device 12 performingan operation if it is determined that the individual performing cleaninghas not performed a cleaning operation on at least one of the pluralityof target surfaces (154). For example, user interface module 44 ofwearable computing device 12 may receive information from remotecomputing system 14 via network 16 indicating that at least one of thesurfaces targeted for cleaning during the cleaning event has not, infact, had a cleaning operation performed on the surface. User interfacemodule 44 may control wearable computing device 12 in response toreceiving such an indication to perform one or more operations.

For example, user interface module 44 may perform an operation bycontrolling user interface 40 issue at least one of an audible, atactile, and a visual alert. The alert may be a general alert notifyingthe wearer of wearable computing device 12 alert condition or mayprovide more specific information to the wearer about the content ofalert. For example, the user alert may indicate via audible and/orvisual (e.g., textual) delivery that the individual performing cleaninghas not performed a cleaning operation on at least one of the targetsurfaces. In some examples, the user alert outputs informationidentifying the specific surface on which the user has not performed thecleaning operation, e.g., by describing the name or other identifyinginformation of the target surface. In other implementations, wearablecomputing device 12 may perform an operation by communicating with anexternal system, such as a scheduling system, training system, or othersystem which utilizes data indicative of cleaning and/or hygieneperformance.

The operation performed by wearable computing device 12 may be performedat any desired time, e.g., after determining that a cleaning operationhas not been performed on target surface. For example, the operationcontrolling wearable computing device 12 to indicate that a cleaningoperation was not performed on a target surface may be performed afterthe cleaning event is complete, e.g., as part of a training exerciseand/or cleaning quality control evaluation. In other examples, theoperation may be performed to issue an alert via wearable computingdevice 12 in substantially real-time with the performance of thecleaning event. For example, the alert may be issued while theindividual is still performing cleaning is still conducting the cleaningevent and/or in sufficient close enough temporal proximity to thetermination of the cleaning event for the individual to perform acorrective cleaning operation (e.g., performed a cleaning operation onthe one or more missed surfaces targeted for cleaning).

To help facilitate cleaning compliance and/or provide substantiallyreal-time cleaning efficacy feedback, the individual performing cleaningmay be instructed to perform a cleaning operation on each of the targetsurfaces in a target order. In other words, the individual performingcleaning may have a dictated sequential order in which the surfaces areto be cleaned. Cleaning efficacy determination module 26 can determinean order in which each surface on which a cleaning operation wasperformed was cleaned. Cleaning efficacy determination module 26 cancompare the surface cleaning order to a target order in which eachsurface is expected to be cleaned, e.g., and determine if there anydeviations between the actual order of cleaning in the target order ofcleaning (e.g., stored in a data store of remote computing system 14and/or wearable computing device 12). For example, cleaning efficacydetermination module 26 may perform the order analysis in substantiallyreal-time with the cleaning event, e.g., as a cleaning operation isperformed on each surface, and may determine in substantially real-timewith a target surface has been missed. Such target surface may be missedin that the individual performing cleaning forgot to perform a cleaningoperation on the surface or in that the individual performing cleaninghas neglected to clean the surface in the target order and has not yetreturned to clean the surface.

In response to determining that the individual performing cleaning hasnot performed a cleaning operation on each of the plurality of targetsurfaces in the target order, a user alert may be issued by wearablecomputing device 12. The user alert may be any of the foregoingdescribed user alerts and may or may not contain information identifyingthe incorrect order of cleaning operations performed. Additionally oralternatively, the information may be stored in a data store associatedwith wearable computing device 12 and/or remote computing system 14identifying the order of cleaning operations performed (e.g., order ofsurfaces cleaned), optionally with a timestamp corresponding to thecleaning and/or information identifying the target order of cleaning.

In the example technique of FIG. 8, cleaning validation information maybe stored in a data store associated with wearable computing device 12and/or remote computing system 14 in addition to or in lieu ofperforming an operation (156). For example, in instances where cleaningefficacy determination module 26 determines that the individualperforming cleaning has cleaned each of the plurality of targetsurfaces, cleaning validation information associated with the pluralityof target surfaces, a time of the cleaning event (e.g. a time stamp),and/or other metadata corresponding to the context of measurement (e.g.,room identification, GPS location) may be stored in a data store.Movement data generated by sensor device 42 associated with the cleaningevent may or may not also be stored as part of a clean validationinformation. In either case, the cleaning validation information mayprovide quantifiable evidence that the individual performing cleaninghas, in fact, performed the cleaning according to the required protocolstandards. While cleaning validation information associated withcompliant cleaning behavior may be stored, it should be appreciated thatinformation associated with non-compliant behavior (e.g., cleaning notperformed on all target surfaces) may also be stored, e.g., fortraining, analysis, and improvement.

In some implementations, cleaning efficacy determination module 26 mayalso evaluate a quality of cleaning performed by the wearer of wearablecomputing device 12 on one or more of the target surfaces deemed havebeen cleaned (e.g., on which a cleaning operation was performed). In oneexample, cleaning efficacy determination module 26 may compare aduration of a cleaning operation performed on a target surface to athreshold duration stored in a data store corresponding to a quality ofcleaning. The threshold duration may specify a minimum amount of timeeach target surface should be cleaned, which may vary depending on thesize and shape of the object and tendency to become contaminated. Ifcleaning efficacy determination module 26 determines that the durationof the cleaning operation performed on the target surface was equal toor greater than the threshold duration, the module may determine thatthe quality of clean performed on the target surface satisfied thethreshold quality of cleaning.

Additionally or alternatively, cleaning efficacy determination module 26may analyze movement data associated with cleaning of a specific targetsurface to reference movement data associate with a quality of cleaningof that target surface in data store 30. Additional details on anexample process by which cleaning efficacy determination module 26 maydetermine a quality of cleaning with reference to data store 30 isdescribed with respect to FIG. 9 below.

FIG. 9 is a flowchart illustrating example operation of an examplewearable computing device configured to track cleaning efficacy forreducing illnesses and infections caused by ineffective cleaning inaccordance with one or more additional aspects of the presentdisclosure. The technique shown in FIG. 9 may be performed by one ormore processors of a computing device, such as wearable computing device12 and/or remote computing system 14.

In the example technique of FIG. 9, wearable computing device 12 candetect movement associated with the device during a cleaning event(160). The movement may be generated by an individual performingcleaning during the cleaning event, with a target surface intended to becleaned to a threshold quality of cleaning during the event. Wearablecomputing device 12 may detect movement via sensor device 42 andgenerate movement data corresponding to the movement.

The surfaces targeted for cleaning to a threshold quality of cleaningduring the cleaning event may be any surface and object discussedherein, including those discussed with respect to FIG. 4. The individualperforming cleaning during the cleaning event may be instructed to cleanthe surface following a cleaning protocol, e.g., which may be the sameprotocol used to generate reference movement data corresponding to athreshold quality of cleaning and stored in data store 30.

At least one sensor of wearable computing device 12 may generatemovement data corresponding to movement during the cleaning operation.One or more processors of wearable computing device 12 may receive thegenerated movement data and control transmission of the movement data,or data derived therefrom, to remote computing system 14. One or moreprocessors 50 executing on remote computing system 14 may receive thedata and execute instructions that cause cleaning efficacy determinationmodule 26 to evaluate and efficacy of the cleaning performed.

Cleaning efficacy determination module 26 executing on remote computingsystem 14 may determine whether the individual performing cleaning hascleaned the target surface with a threshold quality of cleaning (162).Cleaning efficacy determination module 26 can compare movement datagenerated by sensor device 42 of wearable computing device 12 during thecleaning event with reference movement data associated with a thresholdquality of cleaning for the surface stored in data store 30 to make suchdetermination.

In some implementations, cleaning efficacy determination module 26determines at least one signal feature for the received movement data tocompare the movement data generated during the cleaning event to thereference movement data. For example, cleaning efficacy determinationmodule 26 may determine a plurality of signal features for the receivedmovement data generated by sensor device 42 during the cleaning event.The one or more signal features generated for the received movement datamay correspond to those features selected during a calibration processto establish a quality of cleaning for the surface. For example, the oneor more signal features may correspond to those discussed above withrespect to FIGS. 4 and 5. Cleaning efficacy determination module 26 maycompare the one or more signal features determined for the movement datagenerated during the cleaning event with reference signal feature datagenerated during calibration and stored in data store 30 correspondingto a quality of cleaning for the surface, e.g., as discussed above withrespect to FIG. 5.

When wearable computing device 12 is implemented with multiple sensors(e.g., including an accelerometer and a gyroscope), each of the multiplesensors may generate corresponding movement data during the cleaningevent. Cleaning efficacy determination module 26 executing on remotecomputing system 14 may determine one or more signal features based onmovement data generated by and received from each of the plurality ofsensors. For example, cleaning efficacy determination module 26 mayreceive first movement data corresponding to an acceleration of wearablecomputing device 12 and second movement data corresponding to an angularvelocity of the wearable computing device (for a gyroscope). Cleaningefficacy determination module 26 may determine at least one signalfeature based on the first movement data and at least one additionalsignal feature based on the second movement data to characterize themovement performed during the cleaning event.

In some examples, cleaning efficacy determination module 26 receivesmovement data generated throughout the duration of the cleaning eventthat includes movement other than that associated with a cleaningoperation being performed on the target surface. For example, themovement data may include periods of cleaning action and non-cleaningaction. As another example, the movement data may include periods inwhich surfaces other than the target surface whose cleaning quality isbeing evaluated are cleaned.

Cleaning efficacy determination module 26 may segregate the movementdata received from sensor device 42 by associating different portions ofthe movement data to different cleaning actions. For example, cleaningefficacy determination module 26 may associate a portion of the movementdata received during the cleaning event with a time when the targetsurface is being cleaned. Cleaning efficacy determination module 26 mayassociate a portion of movement data with a particular surface beingcleaned using any suitable technique, including those associationtechniques described above with respect to FIG. 4. Additionally oralternatively, cleaning efficacy determination module 26 mayalgorithmically break the movement data into periods corresponding tocleaning activity and non-cleaning activity, e.g., based on featureanalysis of the movement data.

Accordingly, in some examples, cleaning efficacy determination module 26may determine one or more signal features indicative of a quality ofcleaning for only that portion of movement data corresponding to whenthe target surface is being cleaned, e.g., as opposed to the entireduration of the cleaning event. Cleaning efficacy determination module26 can then compare the one or more signal features generated based onthe associate movement data to reference movement data stored in datastore 30.

In some examples, the reference movement data stored in data store 30corresponds to a thoroughness of cleaning (e.g., indicative of the cleantechnique used and/or amount of work applied in performing thecleaning). Additionally or alternatively, the reference movement datastored in data store 30 may correspond to an area or extent of thetarget surface to be cleaned. For example, the reference movement datamay define boundaries for the target surface in three-dimensional space.In these examples, cleaning efficacy determination module 26 candetermine an area of cleaning performed on the target surface based ondata generated by sensor device 42. The area of cleaning may correspondto a two or three-dimensional space over which a cleaning operation wasperformed. Accordingly, cleaning efficacy determination module 26 maydetermine a quality of cleaning by comparing an area of cleaningperformed on the target surface to reference area data on the targetsurface stored in data store 30. Cleaning efficacy determination module26 may determine if the area of cleaning performed on the target surfaceis greater than a threshold target area to be cleaned, e.g., todetermine whether the cleaning operation satisfies the threshold qualityof cleaning.

The technique of FIG. 9 includes wearable computing device 12 performingan operation if it is determined that the individual performing cleaninghas not performed a threshold quality of cleaning on the surface (156).For example, user interface module 44 of wearable computing device 12may receive information from remote computing system 14 via network 16indicating that the surface targeted for cleaning during the cleaningevent has not been cleaned to the threshold quality of cleaning. Userinterface module 44 may control wearable computing device 12 in responseto receiving such an indication to perform one or more operations.

For example, user interface module 44 may perform an operation bycontrolling user interface 40 issue at least one of an audible, atactile, and a visual alert. The alert may be a general alert notifyingthe wearer of wearable computing device 12 alert condition or mayprovide more specific information to the wearer about the content ofalert. For example, the user alert may indicate via audible and/orvisual (e.g., textual) delivery that the individual performing cleaninghas not performed a cleaning operation on a surface to a thresholdquality of cleaning.

The operation performed by wearable computing device 12 may be performedat any desired time. For example, the operation controlling wearablecomputing device 12 to indicate that a threshold quality of cleaning wasnot performed on a surface may be performed after the cleaning event iscomplete, e.g., as part of a training exercise and/or cleaning qualitycontrol evaluation. In other examples, the operation may be performed toissue an alert via wearable computing device 12 in substantiallyreal-time with the performance of the cleaning event. For example, thealert may be issued while the individual is still performing cleaningand/or in sufficiently close temporal proximity to the termination ofthe cleaning event for the individual to perform a corrective cleaningoperation (e.g., further clean the surface).

In some implementations, cleaning validation information may be storedin a data store associated with wearable computing device 12 and/orremote computing system 14 in addition to or in lieu of performing anoperation (166). For example, in instances where cleaning efficacydetermination module 26 determines that the individual performingcleaning has cleaned the target surface to the threshold quality ofcleaning, cleaning validation information associated with the surfaces,a time of the cleaning event (e.g. a time stamp), and/or other metadatacorresponding to the context of measurement (e.g., identification of thesurface, GPS location) may be stored in a data store. Movement datagenerated by sensor device 42 associated with the cleaning event may ormay not also be stored as part of the cleaning validation information.In either case, the cleaning validation information may providequantifiable evidence that the individual performing cleaning has, infact, performed the cleaning according to the required qualitystandards. While cleaning validation information associated withcompliant cleaning behavior may be stored, it should be appreciated thatinformation associated with non-compliant behavior (e.g., cleaning notsatisfying a threshold quality of cleaning) may also be stored, e.g.,for training, analysis, and improvement.

FIG. 10 is a flowchart illustrating example operation of an examplewearable computing device configured to track cleaning efficacy fortotal hygiene management in accordance with one or more aspects of thepresent disclosure. The technique shown in FIG. 10 may be performed byone or more processors of a computing device, such as wearable computingdevice 12 and/or remote computing system 14.

In the example technique of FIG. 10, wearable computing device 12 cangenerate movement data during a course of activity that may includecleaning actions and non-cleaning actions (178). The movement data maybe generated by an individual performing activity, e.g., during acleaning event. The clean activity may correspond to one or morespecific types of clean actions, whereas the non-cleaning actions maycorrespond to movement before, between, and/or after cleaning actions.

At least one sensor of wearable computing device 12 may generatemovement data corresponding to movement by the wearer of the wearablecomputing device, e.g., during cleaning and non-cleaning actions. One ormore processors of wearable computing device 12 may receive thegenerated movement data and control transmission of the movement data,or data derived therefrom, to remote computing system 14. One or moreprocessors 50 executing on remote computing system 14 may receive thedata and execute instructions that cause cleaning efficacy determinationmodule 26 to evaluate an efficacy of the cleaning performed.

Cleaning efficacy determination model 26 executing on remote computingsystem 12 may determine at least one feature of the movement dataindicating that the wearer of wearable computing device 12 is performinga cleaning action (180). The one or more signal features generated forthe received movement data may correspond to those features selectedduring a calibration process to discriminate cleaning from non-cleaningactions. For example, the one or more signal features may correspond tothose discussed above with respect to FIGS. 4 and 6. Cleaning efficacydetermination module 26 may compare the one or more signal featuresdetermined for the movement data generated received from wearablecomputing device 12 with reference signal feature data generated duringcalibration and stored in data store 32.

When wearable computing device 12 is implemented with multiple sensors(e.g., including an accelerometer and a gyroscope), each of the multiplesensors may generate corresponding movement data during the cleaningevent. Cleaning efficacy determination module 26 executing on remotecomputing system 14 may determine one or more signal features based onmovement data generated by and received from each of the plurality ofsensors. For example, cleaning efficacy determination module 26 mayreceive first movement data corresponding to an acceleration of wearablecomputing device 12 and second movement data corresponding to an angularvelocity of the wearable computing device (for a gyroscope). Cleaningefficacy determination module 26 may determine at least one signalfeature based on the first movement data and at least one additionalsignal feature based on the second movement data to characterize themovement performed during the cleaning event.

Example types of cleaning actions that may be performed includeenvironmental cleaning actions in which one or more surfaces inenvironment 18 are cleaned. Examples of these types of cleaning actionsinclude floor surface cleaning actions (e.g., sweeping, mopping) andnon-floor surface cleaning actions (e.g., cleaning equipment within anenvironment 18). Another type of cleaning action that may be performedis a personal cleaning action, such as a hand hygiene cleaning event inwhich an individual conducts a handwashing protocol (e.g., with analcohol-containing sanitizer, with soap and water). By contrast,non-cleaning actions may be any activity the generates movement data notassociated with personal or environmental cleaning activity.

The example technique of FIG. 10 further includes determining a specifictype of cleaning action performed from the movement data generated bywearable computing device 12 (182). For example, cleaning efficacydetermination model 26 executing on remote computing system 12 maydetermine at least one feature of the movement data corresponding toeach of the multiple types of clean actions performed and for whichmovement data was generated by wearable computing device 12. The one ormore signal features generated for the received movement data maycorrespond to those features selected during a calibration process todiscriminate each specific type of cleaning activity from each otherspecific type of cleaning activity. For example, the one or more signalfeatures may correspond to those discussed above with respect to FIGS. 4and 6. Cleaning efficacy determination module 26 may compare the one ormore signal features determined for the movement data associated witheach cleaning activity with reference signal feature data generatedduring calibration and stored in data store 32.

In the example of FIG. 10, cleaning efficacy determination module 26 mayanalyze movement data associated with one or more specific types ofcleaning actions with reference to movement data associated with aquality of cleaning of that specific type of cleaning action in datastore 30 (184). Additional details on an example process by whichcleaning efficacy determination module 26 may determine a quality ofcleaning for a specific type of cleaning action with reference to datastore 30 is described with respect to FIG. 9 above.

In some implementations, the individual performing multiple cleaningactions may be instructed to perform each cleaning action in a targetorder. In other words, the individual performing cleaning may have adictated sequential order in which different cleaning actions are to beperformed. For example, the dictated order may specify that theindividual perform all non-hand-hygiene cleaning actions and thenperform a hand hygiene cleaning action (e.g., before then performingnon-cleaning activities).

Cleaning efficacy determination module 26 can determine an order inwhich each specific type of cleaning action was performed. Cleaningefficacy determination module 26 can compare the cleaning action orderto a target order in which each action is expected to be performed,e.g., and determine if there any deviations between the actual order ofcleaning and the target order of cleaning (e.g., which may be stored ina data store of remote computing system 14 and/or wearable computingdevice 12). For example, cleaning efficacy determination module 26 mayperform the order analysis in substantially real-time with the cleaningactions being performed.

In response to determining that the individual wearing wearablecomputing device 12 has not performed each cleaning action in the targetorder, a user alert may be issued by wearable computing device 12. Theuser alert may be any of the foregoing described user alerts and may ormay not contain information identifying the incorrect order of cleaningactions performed. Additionally or alternatively, the information may bestored in a data store associated with wearable computing device 12and/or remote computing system 14 identifying the order of cleaningactions performed, optionally with a timestamp corresponding to thecleaning and/or information identifying the target order of cleaning.

The technique of FIG. 10 includes wearable computing device 12performing an operation if it is determined that the individualperforming a specific type of cleaning action has not performed athreshold quality of cleaning for the action (186). For example, userinterface module 44 of wearable computing device 12 may receiveinformation from remote computing system 14 via network 16 indicatingthat the specific cleaning action (e.g., hand hygiene action or non-handhygiene action) has not been performed to the threshold quality ofcleaning. User interface module 44 may control wearable computing device12 in response to receiving such an indication to perform one or moreoperations.

For example, user interface module 44 may perform an operation bycontrolling user interface 40 issue at least one of an audible, atactile, and a visual alert. The alert may be a general alert notifyingthe wearer of wearable computing device 12 alert condition or mayprovide more specific information to the wearer about the content ofalert. For example, the user alert may indicate via audible and/orvisual (e.g., textual) delivery that the individual performing cleaninghas not performed a cleaning action to a threshold quality of cleaning.In other examples, the operation may be performed to issue an alert viawearable computing device 12 in substantially real-time with theperformance of the cleaning action. For example, the alert may be issuedwhile the individual is still performing cleaning action and/or insufficiently close temporal proximity to the termination of the cleaningaction for the individual to perform a corrective cleaning action (e.g.,further clean).

In some implementations, cleaning validation information may be storedin a data store associated with wearable computing device 12 and/orremote computing system 14 in addition to or in lieu of performing anoperation (188). Movement data generated by sensor device 42 associatedwith cleaning action(s) may or may not also be stored as part of thecleaning validation information. In either case, the cleaning validationinformation may provide quantifiable evidence that the individualperforming cleaning has, in fact, performed certain cleaning actionsand/or performed the cleaning action(s) according to the requiredquality standards. While cleaning validation information associated withcompliant cleaning behavior may be stored, it should be appreciated thatinformation associated with non-compliant behavior (e.g., cleaning notsatisfying a threshold quality of cleaning) may also be stored, e.g.,for training, analysis, and improvement.

As initially discussed above with respect to FIG. 1, user interfacemodule 44 may cause user interface 40 of wearable computing device 12 topresent audio (e.g., sounds), graphics, or other types of output (e.g.,haptic feedback, etc.) associated with a user interface. The output maybe responsive to one or more cleaning determinations made and, in someexamples, may provide cleaning information to the wearer of wearablecomputing device 12 to correct cleaning behavior determined to benoncompliant. For example, when cleaning efficacy determination module26 determines whether or not the user has performed certain compliantcleaning behavior (e.g., performed a cleaning operation on each surfacetargeted for cleaning, cleaned a target surface to a threshold qualityof cleaning, and/or performed a specific type of cleaning action and/orperform such action to a threshold quality of cleaning), user interfacemodule 44 may control wearable computing device 12 to output an alertconcerning the compliant or non-compliant action.

In addition to or in lieu of controlling user interface 40 based oncompliance or non-compliance with certain cleaning behavior, userinterface 40 of wearable computing device 12 may provide information tohelp guide a user through a cleaning protocol. For example, userinterface 40 may provide audible, tactile, and/or visual informationinforming the user of wearable computing device 12 of the cleaningprotocol to be performed. The information may provide step-by-stepinstructions, such as providing an order of surfaces to be cleanedand/or order of cleaning techniques to be performed on one or moresurfaces being cleaned.

In some implementations, completion of a specific step of a cleaningprotocol (e.g., cleaning a specific surface, using a specific cleaningtechnique on a surface) is automatically detected based on movement datagenerated by wearable computing device 12. User interface 40 may issueinformation informing the user of the next step of the cleaning protocolto be performed in response to automatically detecting completion of thepreceding step of the protocol. Additionally or alternatively, a usermay interact with user interface 40 to manually indicate that a specificstep of a cleaning protocol has been completed and/or navigate toguidance output for a different step of the cleaning protocol. Userinterface 40 may issue information informing the user of the step of thecleaning protocol to be performed in response to the manual input of theuser, such as information informing the user of the next step of thecleaning protocol to be performed in response to an indication that thepreceding step of the protocol was completed.

FIGS. 17A-17D illustrate of an example sequential series of userinterface graphics that may be displayed to a user to help guideexecution of a cleaning protocol. FIG. 17A illustrates an examplewearable computing device 12 with an image of a dresser or bedsidetable, guiding the user to clean the dresser or bedside table. FIG. 17Billustrates the example wearable computing device with an image of atray table, guiding the user to clean the tray table after completingcleaning of the dresser or bedside table. FIG. 17C illustrates theexample wearable computing device with an image of a chair, guiding theuser to clean the chair after completing cleaning of the tray table.FIG. 17D illustrates the example wearable computing device with an imageof a light switch, guiding the user to clean the light switch aftercompleting cleaning of the chair.

In the examples described above, the functions described may beimplemented in hardware, software, firmware, or any combination thereof.If implemented in software, the functions may be stored on ortransmitted over, as one or more instructions or code, acomputer-readable medium and executed by a hardware-based processingunit. Computer-readable media may include computer-readable storagemedia, which corresponds to a tangible medium such as data storagemedia, or communication media including any medium that facilitatestransfer of a computer program from one place to another, e.g.,according to a communication protocol. In this manner, computer-readablemedia generally may correspond to (1) tangible computer-readable storagemedia, which is non-transitory or (2) a communication medium such as asignal or carrier wave. Data storage media may be any available mediathat can be accessed by one or more computers or one or more processorsto retrieve instructions, code and/or data structures for implementationof the techniques described in this disclosure. A computer programproduct may include a computer-readable medium.

By way of example, and not limitation, such computer-readable storagemedia can comprise RAM, ROM, EEPROM, CD-ROM or other optical diskstorage, magnetic disk storage, or other magnetic storage devices, flashmemory, or any other medium that can be used to store desired programcode in the form of instructions or data structures and that can beaccessed by a computer. Also, any connection is properly termed acomputer-readable medium. For example, if instructions are transmittedfrom a website, server, or other remote source using a coaxial cable,fiber optic cable, twisted pair, digital subscriber line (DSL), orwireless technologies such as infrared, radio, and microwave, then thecoaxial cable, fiber optic cable, twisted pair, DSL, or wirelesstechnologies such as infrared, radio, and microwave are included in thedefinition of medium. It should be understood, however, thatcomputer-readable storage media and data storage media do not includeconnections, carrier waves, signals, or other transient media, but areinstead directed to non-transient, tangible storage media. Disk anddisc, as used herein, includes compact disc (CD), laser disc, opticaldisc, digital versatile disc (DVD), floppy disk and Blu-ray disc, wheredisks usually reproduce data magnetically, while discs reproduce dataoptically with lasers. Combinations of the above should also be includedwithin the scope of computer-readable media.

Instructions may be executed by one or more processors, such as one ormore digital signal processors (DSPs), general purpose microprocessors,application specific integrated circuits (ASICs), field programmablelogic arrays (FPGAs), or other equivalent integrated or discrete logiccircuitry. Accordingly, the term “processor,” as used herein may referto any of the foregoing structure or any other structure suitable forimplementation of the techniques described herein. In addition, in someaspects, the functionality described herein may be provided withindedicated hardware and/or software modules. Also, the techniques couldbe fully implemented in one or more circuits or logic elements.

The techniques of this disclosure may be implemented in a wide varietyof devices or apparatuses. Various components, modules, or units aredescribed in this disclosure to emphasize functional aspects of devicesconfigured to perform the disclosed techniques, but do not necessarilyrequire realization by different hardware units. Rather, as describedabove, various units may be combined in a hardware unit or provided by acollection of interoperative hardware units, including one or moreprocessors as described above, in conjunction with suitable softwareand/or firmware.

The following example may provide additional details on hygiene trackingand compliance systems and techniques according to the disclosure.

EXAMPLE

An experiment was performed to evaluate the ability to track and/ormonitor cleaning activity using a wearable device. The experiment wasreplicated several times using different datalogger apps executing on amobile phone as well as standalone devices (e.g., smart watch) that wereaffixed to various anatomical locations (wrist, arm, pocket). Theresults provided by each device configuration were consistent.

In this specific example, a wrist-worn inertial measurement unit (IMU)having a three-axis accelerometer and three-axis gyroscope was utilizedto obtain measurement data. A single subject performed a cleaningsequence as follows: (1) 4 slow back-and-forth wipes of a table topfollowed by (2) 7 quick scrubs of the table top followed by (3) a singleslow circular wipe of the table top. The subject paused for severalsecond between each of the cleaning motions.

The wrist-worn IMU generated raw data sampled at 50 Hz and included of 6quantities: (1) Linear acceleration in the x-, y-, and z-axes (sampledfrom a triaxial accelerometer) and (2) Rotation rate about the x-, y-,and z-axes (sampled from a triaxial gyroscope). The experimental sessionlasted 126 seconds and produced 126×50=6300 rows of these 6 values intime series. FIGS. 11 and 12 are plots of the linear acceleration androtation rate data generated during the experiment.

The session was video-recorded and two sequences of activity labels wereproduced: (1) A binary target: cleaning or not cleaning, and (2) Amulticlass target: wiping, scrubbing, or not cleaning. Supervisedlearning involves training a model from an initial training set oflabeled data, and the given target sequence determines the type ofpredictive model the pipeline will train (binary or multiclass). Forsimplicity, only a technique variation (wiping vs scrubbing) is includedin this experiment to create a multiclass target. In general, many othermulticlass labels are possible, including: tool used, target ofcleaning, technique of cleaning, or any combination thereof.

The wearable IMU data was filtered for further processing. The data wassubject to various sources of noise that can impact the signal quality,including a loose-fit for the wearable, contact with a garment, and/orsudden collisions with ambient objects. As such, it was desirable tosmooth the data via a filtering operation. The algorithm used providedan N-point moving median filter that effectively removed undesirablespikes and troughs in the raw data.

After filtering, two sliding windows were passed over the data togenerate a feature matrix: a 1 s time-domain feature window and a 5 sfrequency-domain window. The frequency-domain window completelyoverlapped the time-domain window so that as both windows slide everysecond, only 1 s of new data were covered by the 5 s frequency domainwindow. The frequency-domain window also doubled as a window for thegeneration of wavelet features. FIG. 13 illustrates an example singletime-domain feature representation generated from the raw sample data.

At the next step of the data analysis process, candidate features weregenerated for the data to expose different aspects of the primitivekinetic motions that make up cleaning activities. Each featureillustrates a compact second-by-second representation of the originalraw data. The candidate generation step created features combinatoriallyby applying transformations to base functions (i.e., transforms) indifferent feature families, as discussed above with respect to FIG. 4.For the experimental data, a total of 189 candidate features weregenerated for every second of filtered data, forming a feature vectorfor that second of motion. The time series of all such vectors formed afeature matrix from which feature selection was performed.

During feature selection, a feature selection routine was specified toselect the dimensions which best discriminate the activity targets infeature space. As implemented, the number of top features was aconfigurable parameter at feature selection time, but all 189 received ascore and ranking. For the experimental data, top five features selectedfor binary target classification were as follows:

TABLE 1 Feature selection ranking for experimental data. Feature Scoregz_std 214 gz_window_range 194 sma_sum 191 x_std 189 svm_mean 189

What makes a feature a good candidate is that it separates well theclasses in feature space. Pairs and triples of feature spaces can berendered via scatterplots with the activity classes labeled in differentcolors. FIG. 14 illustrates the top two features determined from thecandidate features for binary classification of the experimental data.The data show these two features provide good linear separabilitybetween the target classes (cleaning, not cleaning). More features wereneeded for accurate classification into more classes (not cleaning,scrubbing, and wiping).

Following feature selection, a feature matrix is appended to thesecond-by-second target labels to make a training set for a supervisedlearning classifier. The exact classification algorithm was a parameterpassed to the pipeline. Various classification algorithms were tested,but the class of ensemble classifiers that tended to perform effective(in both the binary and multiclass setting) for the experimental datawas a random forest classifier. The following Tables are classificationreports for a 10-feature random forest evaluated by 10-foldcross-validation applied to the experimental data:

TABLE 2 Binary Classification Results for sample_session precisionrecall f1-score support cleaning 0.90 0.98 0.94 66 notcleaning 0.98 0.890.93 61 avg/total 0.94 0.94 0.94 127 Total (s): 127, Predicted Positive(s): 72, Actual Positive (s): 66

TABLE 3 Multiclass Classification Results for sample_session precisionrecall f1-score support notcleaning 0.82 0.87 0.84 61 scrubbing 0.850.81 0.83 36 wiping 0.86 0.80 0.83 30 avg/total 0.84 0.83 0.83 127

In the preceding example, the multiclass labeling used to train a modelsegmented cleaning motions by technique alone (wiping vs scrubbing).More generally, a cleaning classifier output can utilize a combinationof tools, targets, and techniques in labeling training samples. Each ofthese may carve out predicted cleaning acts in a more natural way: (1)Tool: The cleaning apparatus being handled by the subject in thecleaning act (e.g., rag, toilet brush, mop, broom, duster); (2) Target:The collection of surfaces that constitute the object the subject iscleaning; (3) Technique: The manner in which the cleaning act isexecuted by the subject. FIG. 15 is a plot showing discrimination ofthree types of tools performed as part of a mock restaurant contextfloorcare study utilizing movement data: a broom (sweeping), a mop, anda deck brush. FIG. 16 is a plot showing discrimination of five targetsurfaces performed as part of a mock hospital context study utilizingmovement data.

The invention claimed is:
 1. A method of controlling cleaningeffectiveness comprising: detecting, by a wearable computing device thatis worn by an individual performing cleaning on a target surface,movement associated with the wearable device during a cleaning event;determining, based on the movement associated with the wearablecomputing device, a quality of cleaning for the target surface by atleast comparing movement data generated by the wearable device withreference movement data associated with a threshold quality of cleaningfor the target surface, wherein the reference movement data comprisesreference movement data obtained during at least one training episode inwhich a user cleans the target surface or an equivalent thereof whilewearing the wearable computing device or an equivalent thereof; andresponsive to determining that the target surface has not beeneffectively cleaned to the threshold quality of cleaning, performing, bythe wearable computing device, an operation.
 2. The method of claim 1,further comprising, responsive to determining that the target surfacehas been effectively cleaned to the threshold quality of cleaning,storing cleaning validation information associated with the targetsurface and a time of the cleaning event.
 3. The method of claim 1,wherein: detecting movement associated with the wearable computingdevice comprises receiving, from at least one sensor of the wearablecomputing device, movement data, and determining the quality of cleaningfor the target surfaces comprises: determining at least one signalfeature for the movement data, and comparing the at least one signalfeature for the movement data to reference signal feature dataassociated with cleaning of the target surface.
 4. The method of claim1, wherein performing the operation comprises issuing one of an audible,a tactile, and a visual alert via the wearable computing device.
 5. Themethod of claim 1, wherein performing the operation comprises issuing auser alert indicating that the target surface has not been effectivelycleaned.
 6. The method of claim 1, further comprising receiving, by thewearable computing device, an indication from the individual performingcleaning that there has been a deviation from a planned cleaningprotocol during the cleaning event.
 7. The method of claim 1, whereinthe determining the quality of cleaning for the target surface comprisesat least: associating a portion of the movement data received during thecleaning event with a time when the target surface is being cleaned,determining at least one signal feature indicative of the quality ofcleaning for the portion of the movement data corresponding to when thetarget surface is being cleaned, and comparing the at least one signalfeature indicative of the quality of cleaning for the portion ofmovement data to reference signal feature data associated with thequality of cleaning.
 8. The method of claim 1, wherein determining thequality of cleaning for the target surface comprises at least:associating a portion of the movement data received during the cleaningevent with a time when the target surface is being cleaned, determiningan area of cleaning performed on the target surface, and comparing thearea of cleaning determined to be performed on the target surface toreference area data associated with the target surface.
 9. The method ofclaim 1, wherein performing the operation comprises issuing an alert viathe wearable computing device indicating that the quality of cleaning ofthe target surface is less than the threshold quality of cleaning, thealert being issued in substantially real-time with the target surfacebeing cleaned by the individual performing cleaning.
 10. The method ofclaim 1, further comprising: wirelessly transmitting movement datagenerated by the wearable computing device to one or more remotecomputing devices, determining, at the one or more remote computingdevices, whether the individual performing cleaning has effectivecleaned the target surface, wireless transmitting from the one or moreremote computing devices to the wearable computing device dataindicating that target surface has not been effectively cleaned, andresponsive to the wearable computing device receiving the dataindicating that the target surfaces has not been effective cleaned,performing, by the wearable computing device, the operation.
 11. Awearable computing device comprising: at least one sensor configured todetect movement associated with the wearable computing device; at leastone processor; and a memory comprising instructions that, when executed,cause the at least one processor to: receive, from the at least onesensor, movement data for the wearable computing device while anindividual wearing the wearable computing device performs a cleaningoperation on a target surface during a cleaning event; determine, basedon the movement data, a quality of cleaning for the target surface by atleast comparing movement data with reference movement data associatedwith a threshold quality of cleaning for the target surface, wherein thereference movement data comprises reference movement data obtainedduring at least one training episode in which a user cleans the targetsurface or an equivalent thereof while wearing the wearable computingdevice or an equivalent thereof; and responsive to determining that thetarget surface has not been effectively cleaned to the threshold qualityof cleaning, perform an operation.
 12. The device of claim 11, whereinthe instructions, when executed, cause the at least one processor tostore cleaning validation information associated with the target surfaceand a time of the cleaning event.
 13. The device of claim 11, whereinthe instructions, when executed, cause the at least one processor todetermine the quality of cleaning by at least: determining at least onesignal feature for the movement data, and comparing the at least onesignal feature for the movement data to reference signal feature dataassociated with cleaning of the target surface.
 14. The device of claim11, wherein the instructions, when executed, cause the at least oneprocessor to perform the operation by at least issuing a user alertindicating that the target surface has not been effectively cleaned. 15.The device of claim 11, wherein the instructions, when executed, causethe at least one processor to determine the quality of cleaning for thetarget surface by at least: associating a portion of the movement datawith a time when the target surface is being cleaned, determining atleast one signal feature indicative of the quality of cleaning for theportion of the movement data corresponding to when the target surface isbeing cleaned, and comparing the at least one signal feature indicativeof the quality of cleaning for the portion of movement data to referencesignal feature data associated with the quality of cleaning.
 16. Thedevice of claim 11, wherein the instructions, when executed, cause theat least one processor to determine the quality of cleaning for thetarget surface by at least: associating a portion of the movement datareceived during the cleaning operation with a time when the targetsurface is being cleaned, determining an area of cleaning performed onthe target surface, and comparing the area of cleaning determined to beperformed on the target surface to reference area data associated withthe target surface.
 17. The method of claim 1, wherein the user is atrainer and the at least one training episode comprises at least onetraining episode in which the trainer cleans the target surface or anequivalent thereof while wearing the wearable computing device or anequivalent thereof.
 18. The method of claim 1, wherein the user is theindividual performing cleaning and the at least one training episodecomprises at least one training episode in which the individualperforming cleaning cleans the target surface or an equivalent thereofwhile wearing the wearable computing device or an equivalent thereof.